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					                                               SDCP Baseline Survey Report




        MINISTRY OF LIVESTOCK
            DEVELOPMENT
  SMALLHOLDER DAIRY COMMERCIALIZATION PROGRAMME
        CONTRACT NO: CONS/SDCP/1/2007- 2008

                      BASELINE SURVEY REPORT

            IFAD LOAN NO: 678 KE / GRANT NO. 815-KE
                  IFAD PROJECT NO: KEN/05/F01


                                   JUNE 2009




FIBEC Limited
Bomas of Kenya, Off Forest Edge Road Langata
P.O. Box 10316
00100 GPO Nairobi
Tel: 254-020-892117
Cell: +254 733 223 558 or +254 722 310239
Fax: 254-020-891892
Email: fibeclimited@gmail.com




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                                                                                                                                     SDCP Baseline Survey Report



                                                                 Table of Contents

1      EXECUTIVE SUMMARY .................................................................................................................................. IX
2      INTRODUCTION...............................................................................................................................................1

    2.1        PROGRAMME GOAL .....................................................................................................................................1
    2.2        PROGRAMME PURPOSE .................................................................................................................................1
    2.3        PROGRAMME COMPONENTS ..........................................................................................................................1
    2.4        SCOPE OF THE ASSIGNMENT ...........................................................................................................................2

3      METHODOLOGY ..............................................................................................................................................3
    3.1     OVERVIEW OF THE METHODOLOGY ..................................................................................................................3
    3.2     AREA OF COVERAGE .....................................................................................................................................3
    3.3     DESIGN OF THE STUDY ...................................................................................................................................5
       3.1.1 Sampling ............................................................................................................................................5
       3.1.2 Methods of Data Analysis and Presentation ........................................................................................6
    3.4     TRAINING OF ENUMERATORS ..........................................................................................................................7
    3.5     SOURCES OF DATA AND COLLECTION TECHNIQUES ................................................................................................7
    3.6     SECONDARY DATA SOURCES ...........................................................................................................................7
    3.7     LITERATURE REVIEW .....................................................................................................................................8
    3.8     KEY INFORMANTS.........................................................................................................................................8
    3.9     FOCUS GROUP DISCUSSIONS AND KEY INFORMANT INTERVIEWS ..............................................................................9
    3.10 FIELD VISITS ............................................................................................................................................. 10
    3.11 CASE STUDIES ........................................................................................................................................... 10
    3.12 PHOTOGRAPHS .......................................................................................................................................... 10
4      STUDY FINDINGS ...........................................................................................................................................11
    4.1        NUTRITIONAL STATUS ................................................................................................................................. 11
    4.2        HOUSEHOLDS............................................................................................................................................ 14
    4.3        LEVEL OF EDUCATION .................................................................................................................................. 14
    4.4        HOUSEHOLD SIZE ....................................................................................................................................... 15
    4.5        MAIN OCCUPATION OF HOUSEHOLD HEAD ...................................................................................................... 16
    4.6        LAND SIZE ................................................................................................................................................ 18
    4.7        LAND OWNERSHIP ..................................................................................................................................... 19
    4.8        LAND USE ................................................................................................................................................ 21
    4.9        MILKING HERD .......................................................................................................................................... 23
    4.10       MILK PRODUCTION .................................................................................................................................... 24
    4.11       FARM RECORDS ......................................................................................................................................... 26
    4.12       HOUSEHOLD WELFARE ................................................................................................................................ 28
    4.13       MAIN FEEDS ............................................................................................................................................. 35
    4.14       SUPPLEMENTARY FEEDS ............................................................................................................................... 37
    4.15       COST OF SUPPLEMENTARY FEEDS ................................................................................................................... 37
    4.16       REASONS WHY FARMERS DON’T USE SUPPLEMENTS............................................................................................. 40
    4.17       CONTINGENCY MEASURES TO ENSURE MILK PRODUCTION THROUGHOUT THE YEAR...................................................... 42
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4.18 COST OF MILK PRODUCTION ......................................................................................................................... 45
4.19 WATER SOURCES ....................................................................................................................................... 47
4.20 ADEQUACY OF WATER ................................................................................................................................ 49
4.21 CHOICE OF ANIMAL BREEDS.......................................................................................................................... 50
4.22 PREFERRED BREEDING METHODS ................................................................................................................... 51
4.23 CHOICE OF THE PREFERRED BREEDING METHODS ............................................................................................... 52
4.24 BREEDING RELATED COSTS ........................................................................................................................... 53
4.25 BREEDING EFFICIENCY ................................................................................................................................. 56
4.26 CALVING INTERVAL ..................................................................................................................................... 57
4.27 MILK PRODUCTION, SALES AND CONSUMPTION................................................................................................. 58
   4.27.1   Milk Bars and other milk outlets ................................................................................................... 60
4.28 MILK HANDLING PRACTICES .......................................................................................................................... 61
4.29 MILK MARKETING CONSTRAINTS ................................................................................................................... 63
4.30 MILK PROCESSING ...................................................................................................................................... 65
4.31 SKILLS REQUIRED TO IMPROVE PROFITS IN DAIRY FARMING .................................................................................. 66
4.32 TYPES AND ORGANIZATION OF COMMUNITY GROUPS ......................................................................................... 68
4.33 SIZE OF THE GROUPS................................................................................................................................... 73
4.34 REGISTERED COWS ..................................................................................................................................... 73
4.35 ANIMAL HEALTH MANAGEMENT AND DELIVERY ................................................................................................ 73
   4.35.1   Livestock types and classes most at risk ......................................................................................... 75
   4.35.2   Cost of providing animal health care per herd per month ............................................................... 75
4.36 EMPLOYMENT CREATION IN DAIRY ENTERPRISES ................................................................................................ 76
4.37 BREED DISTRIBUTION .................................................................................................................................. 77
4.38 HERD STRUCTURE ...................................................................................................................................... 79
4.39 COST OF BUYING DAIRY ANIMALS .................................................................................................................. 82
4.40 PRODUCTION SYSTEM ................................................................................................................................. 83
4.41 COST OF ZERO GRAZING .............................................................................................................................. 85
4.42 FARM INFRASTRUCTURE .............................................................................................................................. 85
4.43 COST OF LABOUR ....................................................................................................................................... 86
4.44 CONDITION OF MILKING SHED ..................................................................................................................... 87
4.45 GENDER IN DAIRY ...................................................................................................................................... 88
4.46 GENDER DIVISION OF LABOUR ....................................................................................................................... 90
4.47 SAVINGS AND CREDIT .................................................................................................................................. 94
4.48 LOAN APPLICATIONS ................................................................................................................................... 98
4.49 TYPE OF LENDER ...................................................................................................................................... 100
4.50 LOAN PRODUCTS ..................................................................................................................................... 101
   4.50.1   Loan Size .................................................................................................................................... 101
   4.50.2   Success Rate ............................................................................................................................... 102
   4.50.3   Reasons for Unsuccessful Loan Applications ............................................................................... 103
   4.50.4   Type of Payment ......................................................................................................................... 104
   4.50.5   Loan Repayment Period .............................................................................................................. 105
   4.50.6   Interest Rate ............................................................................................................................... 105
   4.50.7   Type of Collateral Used ............................................................................................................... 108
   4.50.8   Amount Paid at Maturity ........................................................................................................... 110
4.51 NATURAL RESOURCE MANAGEMENT PROBLEMS .............................................................................................. 110
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                                                                                                                                   SDCP Baseline Survey Report


    4.52 USE OF WASTE FROM DAIRY ENTERPRISE ........................................................................................................ 112
    4.53 SEVERITY OF THE NRM PROBLEMS ............................................................................................................... 112
    4.54 HOUSEHOLD ASSETS ................................................................................................................................. 113
       4.54.1  Roof Materials ............................................................................................................................ 113
       4.54.2  Wall Materials ............................................................................................................................ 114
       4.54.3  Floor Materials ........................................................................................................................... 116
       4.54.4  Window materials in use............................................................................................................. 116
    4.55 SUPPORT TO POLICY AND INSTITUTIONS ......................................................................................................... 117

5      CONCLUSIONS AND RECOMMENDATIONS .................................................................................................. 118
    5.1        SUSTAINABILITY ....................................................................................................................................... 118
    5.2        RECOMMENDATIONS ................................................................................................................................ 122




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                                                                                                                                   SDCP Baseline Survey Report




                                                                      List of Figures

FIGURE 1: MAP SHOWING THE AREA COVERED BY THE SURVEY ................................................................................................5
FIGURE 2: EDUCATION LEVEL OF DAIRY FARMERS ............................................................................................................... 14
FIGURE 3: MAIN OCCUPATION OF HOUSEHOLD HEAD IN DCA 1 ............................................................................................ 17
FIGURE 4: MAIN OCCUPATION OF HOUSEHOLD HEADS IN DCA 3 ........................................................................................... 17
FIGURE 5: LAND USE ................................................................................................................................................... 22
FIGURE 6: AVERAGE HERD SIZE BY DISTRICT IN THE PROJECT AREA........................................................................................... 23
FIGURE 7: DISTRIBUTION OF MILK PRODUCTION ACROSS THE SDCP AREA ................................................................................. 26
FIGURE 8: MEAN MONTHLY HOUSEHOLD EXPENDITURE ...................................................................................................... 29
FIGURE 9: MAP SHOWING THE MEAN HOUSEHOLD EXPENDITURE .......................................................................................... 34
FIGURE 10: MAIN ANIMAL FEEDS IN THE PROJECT AREA ...................................................................................................... 36
FIGURE 11: MAIN ANIMAL FEEDS BY DISTRICT ................................................................................................................... 37
FIGURE 12: AVERAGE DAILY COST OF SUPPLEMENTARY FEEDS IN DRY SEASON .......................................................................... 39
FIGURE 13: AVERAGE COSTS OF MILK PRODUCTION (WET SEASON) ....................................................................................... 40
FIGURE 14: FEED CONTINGENCY MEASURES IN DCA 1 ........................................................................................................ 43
FIGURE 15: FEED CONTINGENCY MEASURES IN DCA 3 ........................................................................................................ 43
FIGURE 16: COST OF MILK PRODUCTION DURING THE DRY SEASON ........................................................................................ 46
FIGURE 17: COST OF MILK PRODUCTION DURING THE WET SEASON ........................................................................................ 46
FIGURE 18: MAIN SOURCES OF WATER DURING THE WET SEASON ......................................................................................... 47
FIGURE 19: PREFERENCE FOR BULL SERVICE BY DISTRICT IN DCA1 AND DCA 3 ......................................................................... 52
FIGURE 20: AVERAGE DAIRY REVENUE FROM MILK SALES IN KSHS.......................................................................................... 60
FIGURE 21: MILK HANDLING PRACTICES ........................................................................................................................... 61
FIGURE 22: ON-FARM MILK PROCESSING ......................................................................................................................... 66
FIGURE 23: SKILLS NEEDED TO INCREASE PROFITABILITY OF DAIRY ENTERPRISE .......................................................................... 67
FIGURE 24: DISTRIBUTION OF DAIRY CATTLE BREEDS IN THE PROJECT AREA ............................................................................. 78
FIGURE 25: MAP SHOWING THE BREED DISTRIBUTION IN THE PROJECT AREA ............................................................................ 79
FIGURE 26: GENDER OF THE HOUSEHOLD HEAD .................................................................................................................. 89
FIGURE 27: GENDER OF THE HOUSEHOLD HEADS BY DISTRICT ............................................................................................... 90
FIGURE 28: DISTRIBUTION OF THE HOUSEHOLDS MAKING SAVINGS ........................................................................................ 95
FIGURE 29: PREFERRED MODE OF SAVING ........................................................................................................................ 96
FIGURE 30: PREFERRED METHODS OF SAVINGS ................................................................................................................. 96
FIGURE 31: LOAN APPLICATION BY MONTH ...................................................................................................................... 99
FIGURE 32: REASONS WHY FARMERS BORROWED THE PREVIOUS SEASON ................................................................................ 100
FIGURE 33: TYPE OF LENDER ....................................................................................................................................... 101
FIGURE 34: LOAN SUCCESS RATE ................................................................................................................................. 103
FIGURE 35: REASONS FOR UNSUCCESSFUL LOAN APPLICATIONS ............................................................................................ 104
FIGURE 36: TYPE OF PAYMENT .................................................................................................................................... 105
FIGURE 37: MEAN LOAN SIZE AND INTEREST RATES .......................................................................................................... 106
FIGURE 38: TYPE OF COLLATERAL ................................................................................................................................. 109
FIGURE 39: PROBLEMS ASSOCIATED WITH NATURAL RESOURCE MANAGEMENT ....................................................................... 111
FIGURE 40: USE OF WASTE FROM DAIRY ENTERPRISE ........................................................................................................ 112
FIGURE 41: SEVERITY OF NRM PROBLEMS ..................................................................................................................... 113
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FIGURE 42: WALL MATERIALS USED IN CONSTRUCTING HOUSEHOLDS ................................................................................... 115

                                                                    List of Tables

TABLE 1: ADMINISTRATIVE AREAS OF DCAS IN THE PROGRAMME AREA .................................................................................4
TABLE 2: NUMBER OF HOUSEHOLDS INTERVIEWED BY DISTRICT AND DCA .............................................................5
TABLE 3: NUTRITION STATUS OF CHILDREN AMONG THE POOR AND NON-POOR HOUSEHOLDS IN THE PROJECT AREA ......................... 11
TABLE 4: HIGHEST EDUCATION LEVEL OF HOUSEHOLD HEADS BY DISTRICT AND DCA ................................................................... 15
TABLE 5: SIZE OF HOUSEHOLD BY DISTRICT AND DCA ............................................................................................. 16
TABLE 6: MAIN OCCUPATION OF THE HOUSEHOLD HEAD BY DISTRICT IN DCA 1 ........................................................................ 18
TABLE 7: MAIN OCCUPATION OF THE HOUSEHOLD HEAD BY DISTRICT IN DCA 3 ........................................................................ 18
TABLE 8: HOW MUCH LAND IS AVAILABLE TO THIS FAMILY? ....................................................................................... 19
TABLE 9: LAND OWNERSHIP BY DISTRICT IN DCA 1............................................................................................................. 20
TABLE 10: LAND OWNERSHIP BY DISTRICT IN DCA 3 ........................................................................................................... 20
TABLE 11: CIRCUMSTANCES OF DAIRY FARMERS WHO DID NOT OWN LAND ................................................................................ 21
TABLE 12: LAND USE IN DCA1 BY DISTRICT ...................................................................................................................... 22
TABLE 13: LAND USE IN DCA 3 BY DISTRICT ...................................................................................................................... 23
TABLE 14: AVERAGE SIZE OF THE MILKING HERD BY BREED BY DISTRICT IN DCA 1 ....................................................................... 24
TABLE 15: AVERAGE SIZE OF THE MILKING HERD BY BREED BY DISTRICT IN DCA 3 ....................................................................... 24
TABLE 16: AVERAGE MILK PRODUCTION OF THE DAIRY HERD IN LITRES/DAY BY DISTRICT .............................................................. 25
TABLE 17: PROPORTION OF HOUSEHOLDS KEEPING FARM RECORDS IN DCA1 AND DCA 3 ........................................................... 27
TABLE 18: TYPE OF FARM RECORDS KEPT BY FARMERS IN DCA 1 AND DCA 3 BY DISTRICT ............................................................ 28
TABLE 19: HOUSEHOLD MONTHLY EXPENDITURE BY TYPE, OCCUPATION AND DISTRICTS IN DCA 1 ................................................ 30
TABLE 20: HOUSEHOLD EXPENDITURE BY SOURCE OF INCOME IN DCA 3 BY DISTRICT AND BY TYPE ................................................. 32
TABLE 21: COST OF WATER IN KSHS PER DAY BETWEEN DCA 1 AND DCA 3 ......................................................... 35
TABLE 22: COST OF WATER IN KSHS PER DAY .......................................................................................................... 35
TABLE 23: MAIN LIVESTOCK FEED IN DCA 1 AND DCA 3 ....................................................................................... 36
TABLE 24: AVERAGE QUANTITY OF SUPPLEMENTARY FEEDS USED DURING THE WET SEASON IN DCA 1 AND DCA 3 ............................. 37
TABLE 25: AVERAGE COST OF SUPPLEMENTARY FEEDS IN KSHS DURING THE WET SEASON IN DCA 1 AND DCA 3 ... 38
TABLE 26: AVERAGE COST OF FEED SUPPLEMENTS DURING THE WET SEASON ......................................................... 38
TABLE 27: REASONS WHY FARMERS DON’T USE SUPPLEMENTS IN DCA 1 BY DISTRICT ................................................................. 41
TABLE 28: REASONS WHY FARMERS DON’T USE SUPPLEMENTS IN DCA 3 BY DISTRICT .................................................................. 42
TABLE 29: FEED CONTINGENCY MEASURES IN DCA 1 .............................................................................................. 44
TABLE 30: FEED CONTINGENCY MEASURES IN DCA 3 .............................................................................................. 45
TABLE 31: MAIN WATER SOURCES IN DCA 1 AND DCA 3 DURING WET SEASON ....................................................... 48
TABLE 32: MAIN WATER SOURCES IN DCA 1 AND DCA 3 DURING DRY SEASON .......................................................................... 48
TABLE 33: MAIN SOURCE OF WATER DURING THE WET SEASON BY DISTRICT .............................................................................. 49
TABLE 34: STATUS OF WATER ADEQUACY THROUGHOUT THE YEAR IN DCA 1 AND DCA 3 .......................................... 49
TABLE 35: W ATER ADEQUACY THROUGHOUT THE YEAR ............................................................................................ 50
TABLE 36 : CHOICE OF BREEDS BY DISTRICTS IN DCA 1 ........................................................................................................ 50
TABLE 37: CHOICE OF BREEDS BY DISTRICTS IN DCA 3......................................................................................................... 51
TABLE 38: STATUS OF PREFERRED BREEDING METHOD IN DCA 1 AND DCA 3 ...................................................................... 51
TABLE 39: REASONS FOR BULL PREFERENCE BETWEEN DCA 1 AND DCA 3 ............................................................ 52
TABLE 40: COST OF AI SERVICE USING LOCAL SEMEN BY DISTRICTS IN DCA 1.......................................................... 53
TABLE 41: COST OF AI SERVICE USING LOCAL SEMEN BY DISTRICTS IN DCA 3 ............................................................................. 54
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TABLE 42: COST OF AI SERVICE USING IMPORTED SEMEN BY DISTRICTS IN DCA 1 ........................................................................ 55
TABLE 43: COST OF AI SERVICE USING IMPORTED SEMEN BY DISTRICTS IN DCA 3 ........................................................................ 55
TABLE 44: COST OF BULL SERVICE IN DCA 1 AND DCA 3 ......................................................................................... 56
TABLE 45: COST OF BULL SERVICE BY DISTRICT IN KSHS .......................................................................................... 56
TABLE 46: MAXIMUM NUMBER OF INSEMINATIONS BEFORE CONCEPTION IN DCA 1 ...................................................... 57
TABLE 47: MAXIMUM NUMBER OF INSEMINATIONS BEFORE CONCEPTION IN DCA 3 ................................................... 57
TABLE 48: THE CALVING INTERVAL IN THE DAIRY HERD (IN DAYS) IN DCA 1 AND DCA 3 .......................................................... 58
TABLE 49: AVERAGE MILK PRODUCTION, SALES AND HOME CONSUMPTION IN DCA 1 AND DCA 3 ......................... 58
TABLE 50: AVERAGE MILK PRICE IN VARIOUS OUTLETS IN DCA 1 AND DCA 3 ............................................................................ 59
TABLE 51: AVERAGE MILK PRODUCTION, SALES AND CONSUMPTION BY DISTRICT .................................................. 59
TABLE 52: MILK HANDLING PRACTICES BY DISTRICT ............................................................................................................. 61
TABLE 53: MILK MARKETING CONSTRAINTS IN DCA 1 ........................................................................................................ 63
TABLE 54: MILK MARKETING CONSTRAINTS IN DCA 3 ........................................................................................................ 65
TABLE 55: MEAN PRODUCTION OF ON-FARM DAIRY PRODUCTS .............................................................................................. 66
TABLE 56: FARMERS WHO NEED SKILLS TO INCREASE PROFITABILITY OF DAIRY ENTERPRISE IN DCA 1 .................................... 67
TABLE 57: FARMERS WHO NEED SKILLS TO INCREASE PROFITABILITY OF DAIRY ENTERPRISE IN DCA 3 .................................... 68
TABLE 58: RESULTS OF FGD ANALYSIS OF COMMUNITY GROUPS IN PROJECT AREA .................................................................... 70
TABLE 59: ORGANIZATIONS REGISTERING CATTLE IN DCA 1 AND DCA 3 ................................................................. 73
TABLE 60: FARMERS WITH CATTLE REGISTERED WITH AT LEAST ONE ASSOCIATION .................................................. 73
TABLE 61: THREE COMMON LIVESTOCK DISEASES REPORTED IN DCA 1 AND DCA 3 ................................................ 74
TABLE 62: MOST COMMON LIVESTOCK DISEASE BY DISTRICT ................................................................................................ 74
TABLE 63: COST OF SECURING ANIMAL HEALTH SERVICES BETWEEN DCA 1 AND DCA 3 BY DISTRICT ............................................... 75
TABLE 64: PERMANENT AND CASUAL EMPLOYEES IN AN AVERAGE DAIRY FARM BY DISTRICT IN PROJECT AREA .................................... 76
TABLE 65: AVERAGE DAIRY HERD AND EMPLOYEES BY DISTRICT ............................................................................................. 77
TABLE 66: DISTRIBUTION OF DAIRY BREEDS BY DISTRICT IN DCA 1....................................................................................... 77
TABLE 67: DISTRIBUTION OF DAIRY BREEDS BY DISTRICT IN DCA 3 ......................................................................................... 78
TABLE 68: DISTRIBUTION OF DAIRY STRUCTURE BY BREED IN DCA 1 ........................................................................................ 80
TABLE 69: DISTRIBUTION OF DAIRY STRUCTURE BY BREED IN DCA 3 ........................................................................................ 81
TABLE 70: MEAN NUMBER OF ANIMALS BY BREED IN DCA 3 ................................................................................................ 82
TABLE 71: AVERAGE COST OF BUYING A DAIRY COW AT SOURCE IN KSHS ................................................................ 83
TABLE 72: DAIRY PRODUCTION SYSTEM IN DCA 1 .............................................................................................................. 84
TABLE 73: DAIRY PRODUCTION SYSTEM IN DCA 3 .............................................................................................................. 84
TABLE 74: COST OF ZERO GRAZING UNITS IN KSHS .................................................................................................. 85
TABLE 75: COST OF OTHER FARM INFRASTRUCTURE IN KSHS ACROSS THE DISTRICTS ............................................. 86
TABLE 76: MONTHLY WAGE BILL FOR PERMANENT EMPLOYEES BETWEEN DCA 1 AND DCA 3 .................................. 86
TABLE 77: MONTHLY WAGE BILL FOR CASUAL EMPLOYEES BETWEEN DCA 1 AND DCA 3 ......................................... 87
TABLE 78: AVERAGE MONTHLY W AGES IN KSHS ...................................................................................................... 87
TABLE 79: CONDITION OF ZERO GRAZING UNIT BETWEEN DCA 1 AND DCA 3 .......................................................... 88
TABLE 80: CONDITION OF MILKING SHED BY DISTRICT .............................................................................................. 88
TABLE 81: GENDER OF HOUSEHOLD HEAD IN DCA 1 AND DCA 3 .......................................................................... 89
TABLE 82: COMPARISON BETWEEN MEN AND WOMEN ROLES IN DAIRY PRODUCING HOUSEHOLDS .......................... 91
TABLE 83: GENDER DIVISION OF LABOUR IN DAIRY PRODUCING HOUSEHOLDS ........................................................... 92
TABLE 84: HOUSEHOLDS MAKING REGULAR SAVINGS FROM THE DAIRY ENTERPRISE IN DCA 1 .......................................... 94
TABLE 85: HOUSEHOLDS MAKING REGULAR SAVINGS FROM THE DAIRY ENTERPRISE IN DCA 3 .......................................... 95
TABLE 86: COMPARISON BETWEEN DCA 1 AND DCA 3 IN TERMS OF WHERE HH MEMBER MAKE THEIR SAVINGS .................... 97
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TABLE 87: ACCESS TO CREDIT IN DCA 1 AND DCA 3 ............................................................................................... 99
TABLE 88: LOAN SIZE IN KSHS............................................................................................................................. 102
TABLE 89: SUCCESS RATE IN DCA 1 AND DCA 3 ............................................................................................................. 103
TABLE 90: REASONS FOR UNSUCCESSFUL LOAN APPLICATIONS IN DCA 1 AND DCA 3 ................................................................ 104
TABLE 91: REPAYMENT PERIOD (MONTHS) IN DCA 1 AND DCA 3 ....................................................................... 105
TABLE 92: INTEREST RATE (P.A) IN DCA 1 AND DCA 3 .......................................................................................... 105
TABLE 93: INTEREST RATES (%) CHARGED BY TYPE OF LENDER ............................................................................. 107
TABLE 94: SIZE AND TERMS OF LOANS IN DCA 1 AND DCA 3 .............................................................................................. 107
TABLE 95: TYPE OF COLLATERAL USED IN DCA 1 AND DCA 3 ............................................................................... 109
TABLE 96: AMOUNT PAID AT MATURITY KSHS ......................................................................................................... 110
TABLE 97: AMOUNT PAID AT MATURITY KSHS ....................................................................................................... 110
TABLE 98: NATURAL RESOURCE MANAGEMENT PROBLEMS BY DITRICT .............................................................. 111
TABLE 99: SEVERITY OF NRM ACROSS THE PROJECT AREA ................................................................................ 112
TABLE 100: ROOF MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD ........................................... 113
TABLE 101: WALL MATERIALS BY DISTRICT AND DCA ....................................................................................................... 114
TABLE 102: FLOOR MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD .......................................... 116
TABLE 103: W INDOW MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD ...................................... 116




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                                                                   SDCP Baseline Survey Report



                            LIST OF ACRONYMS

ABS-TCM   -   African Breeders Service Total Cattle Management
AI        -   Artificial Insemination
AIDS      -   Acquired Immune Deficiency Syndrome
CAIS      -   Central Artificial Insemination Station
CBO       -   Community Based Organizations
DCA       -   Dairy Commercialization Area
DIC       -   Dairy Information Centre
DTI       -   Dairy Training Institute
FGDs      -   Focus Group Discussions
FMD       -   Foot and Mouth Disease
GDP       -   Gross Domestic Product
GTZ       -   German Technical Cooperation
HIV       -   Human Immuno Deficiency Virus
IFAD      -   International Fund for Agricultural Development
IFMIS     -   Integrated Financial Management Information System
ILRI      -   International Livestock Research Institute
KAGRI     -   Kenya National Animal Genetic Resource Institute
KARI      -   Kenya Agricultural Research Institute
KDB       -   Kenya Dairy Board
KDPA      -   Kenya Dairy Processors Association
KEDAPO    -   Kenya Dairy Producers Association
KELRI     -   Kenya Livestock Research Institute
KIHBS     -   Kenya Integrated Household Budget Survey
KLBO      -   Kenya Livestock Breeding Organization
KLMB      -   Kenya Livestock Marketing Board
LCMIS     -   Low-Cost Market Information System
M&E       -   Monitoring and Evaluation
MDGs      -   Millennium Development Goals
MIS       -   Management Information Systems
MOLD      -   Ministry of Livestock Development
NGO       -   Non-Governmental Organization
PEV       -   Post Electoral Violence
SDCP      -   Smallholder Dairy Commercialization Programme
SOW       -   Scope of Work
SPSS      -   Statistical Programme for Social Scientists
SWOT      -   Strengths, Weaknesses, Opportunities and Threats
TOR       -   Terms of Reference
UNHCR     -   United Nations High Commission for Refugees
WWS       -   World Wide Sires




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                                                                                  SDCP Baseline Survey Report


1    EXECUTIVE SUMMARY

The Smallholder Dairy Commercialization Program (SDCP) is funded by Government of the Republic of
Kenya (GOK) and the International Fund for Agricultural Development (IFAD). The Programme covers
nine districts namely; Nakuru, Bungoma, Bomet, Central Kisii, Lugari, Nandi North, Nyamira, Trans
Nzoia and Uasin Gishu. This report highlights the findings of the baseline survey in the programme
districts with particular emphasis on DCA 3 and the implications on project implementation.


To conduct this survey, the study team collected both quantitative and qualitative data from both primary
and secondary sources. The field interviews were conducted between March 20, 2009 and April 3, 2009
and targeted 870 heads of dairy households in the project area. This is about 10% of the smallholder dairy
households in DCA1 and DCA 3 whose estimated population is 8,700 households. However, after outliers
were discarded from the data set, analysis used in this analysis was from 784 households with 5,397
individuals from the nine districts. The sample population comprises of 321 respondents in DCA 1 and
463 respondents in DCA 3. In addition, the study team conducted at least one focus group discussion with
dairy groups in each district and interviewed key informants from among milk bar operators, extension
staff and animal health and AI service providers in the study area. However, these findings should be used
with caution in drawing conclusions on the impact of SDCP interventions on DCA 1 based on the findings
of DCA 3 because interventions in the two areas were not strictly at same time.


The SDCP field staff guided the enumerators in identifying and delineating the areas covered by DCA 1
and DCA 3 in each district. However, the enumerators used their discretion to ensure that they sampled
representative households in the delineated areas by spreading the sampled households across the social
spectrum. Other members of the team analyzed both the qualitative and quantitative data the report and
mapping out the findings.
The study team used different techniques to collect and analyze data and information in this survey. The
data collection techniques used included: review of secondary data, key informant interviews, focus group
discussions, observations and stakeholder workshops. These techniques were carried though desk and field
studies.


Data for DCA 1 is the status report of the SDCP interventions because at the time of the survey, the
implementation had been going on for two years. The data from DCA 3 is the one that will be used as the
benchmark because there was no intervention at the time of the survey. Subsequently, data for DCA 1 and
DCA 3 is not meant for comparative purposes.
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                                                                                SDCP Baseline Survey Report



Key Findings
This survey showed that the average land holding is 4.47 acres in DCA 3. This shows that SDCP
is targeting smallholder farmers. However, SDCP needs to continue refining its targeting strategy
to ensure that the project doesn’t leave out needy groups because there are small pockets of non-
poor dairy households in each DCA.


This study found that 77% of the farmers in the project area relied on pastures as the main feed and 21%
on napier grass. Anything that they fed dairy cows beyond this staple diet was considered to be
supplementary feed. The supplements comprised of maize stover, on-farm feed formulations and
commercial feeds. This survey found that farmers in DCA 3 spent only Kshs 179 in supplementary feeds.
The daily average milk production in DCA 3 was 9.81 litres per day. The low milk production
suggests other constraints such as disease burden may be limiting milk production in DCAs.


Using expenditure as a proxy for income, this survey suggests that the average expenditure was
Kshs 23,642 in DCA 3 per month. SDCP is also targeting relatively poor communities based on
the nutritional and household welfare indicators. Given that the average monthly expenditure of
dairy producing households in the project area is Kshs 23,642, the project will continue facing the
challenge of getting poor households into dairy because the high cost of dairy cows is a
significant barrier to entry in dairy farming. For instance, farmers in DCA 3 paid an average of
Kshs 26,643 for a dairy cow. The high cost of dairy cows is a barrier to investment in the
enterprise by poorer households.


This study suggests that the average dairy household in DCA 3 had an average of 1.15 permanent
employees and 1.37 casuals. This suggests that the farmers in DCA 3 are substituting permanent
employees with casual workers.


The study also found that dairy cows in DCA 3 required an average of 1.44 inseminations before
conception. This suggests that there is need for capacity building on heat detection and improved
service delivery. This conclusion is further confirmed by the fact that the calving interval was
16.2 months in DCA 3.


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                                                                                SDCP Baseline Survey Report



This survey also found that the average cost reduction of delivery of animal health services was
Kshs 427.90 per month while that of AI services was Kshs 828.9. The study further found that
only 41.6% of the farmers in DCA 3 kept records. This suggests that SDCP should refine the
methods used to train farmers and simplify the extension messages to increase rate of adoption. In
addition, 57% of the farmers preferred using bull service rather than AI services. The high
preference for bull service is driven by a combination of high costs and poor reliability of the AI
service providers in many parts of the project area. SDCP needs to intensify efforts to train
farmers in heat detection and monitoring service delivery so as to increase the confidence of
farmers to AI services.


This study found that the average farmer in DCA 3 produced 9.81 litres of milk per day and sold
about 6.04 litres per day. This study therefore suggests that the extra milk produced above this
threshold in DCA 3 is currently retained for home consumption. The study found that only 7.3%
of the farmers in DCA 3 engaged in milk processing. This suggests that there is need to train
more farmers to acquire skills in value addition to increase their incomes.


The average daily revenue from milk sales in the project area is Kshs 154 from the sale of 6.2
litres at average price of Kshs 24.8 per litre. While this provides an income of nearly US$ 2/day,
it is still largely financed by unpaid family labour but in turn the enterprise contributes to family
welfare and nutrition from 3.1 litres of the milk retained on the farm daily.


This study found that 53% of all the farmers in the project area had semi-grazing production
system in the project area but only 11% of the farmers had zero grazing units in good condition.
There is therefore need to train farmers on the importance of zero-grazing system in order to
increase adoption rate.


There is a huge unmet need for information and knowledge on basic animal husbandry and
management especially in feeding both in DCA 3. However, the high costs of producing fodder
appear to outweigh other constraints as the reason for not using supplements in DCA 3. SDCP



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needs to continuously seek technologies that can reduce the cost of producing fodder for more
farmers to adopt the technology.


This study showed that 36% of the households were making regular savings in DCA 3. Accessing
credit is still a major challenge in the project area and the survey showed that only 18.5% of the
households were able to access credit. However, demand for credit is still highly skewed towards
consumption rather than investment. This means that SDCP needs to build partnerships with
other institutions that can develop suitable financial products to meet the needs of the poor dairy
producing households especially the ones without title deeds or those intending to enter into dairy
enterprise.


The survey showed that 30% of the households were female headed and the analysis of daily
activity calendar showed that women performed most of the tasks in the dairy enterprise and
therefore there is need to target women in the training. The study found that the average
household had 6 members and that 94% of all the household heads were literate. This suggests
that SDCP can use written messages to communicate to the target groups.


To improve sustainability of the project interventions, a number of recommendations emerged from this
survey:
   1. SDCP should improve targeting of individuals being trained at two levels. First, SDCP should
          ensure that individuals who manage dairy animals are trained and not community gate keepers.
          Secondly, SDCP should improve the organization of the training to attract more women
          participants by looking at the timing of the training and distance to be covered.

   2. SDCP should encourage community in-kind and cash contributions to meet some of the training
          expenses. This entrenches the values of the market system which is central to commercialization.

   3. SDCP should identify and build capacity of self selected service providers in each community to
          complement the role of the extension workers.

   4. SDCP should support farmer to farmer extension services and facilitate farmers to acquire other
          skills needed to undertake farming as a business.




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    5. SDCP should promote match making between farmers with others outside the project area who
        have important lessons to offer. Some of the groups that could qualify for match making include
        outstanding farmers and cooperatives that have overcome similar challenges to create
        commercially viable dairy businesses that have improved the livelihoods of their families,
        communities and other stakeholders in the business.

    6. SDCP should support interventions that mitigate the negative impact of livestock on climate
        change such as agro-forestry, water harvesting and zero-grazing interventions.



The survey identified nine key interventions that SDCP needs to put in place:

    1. This baseline survey recommends SDCP should strengthen group organization and development
        through capacity building activities in DCA 3 to bring about sustainable community and
        institutional transformation.

    2. Provide technical support and technology transfer

    3. Besides improving the technical skills in dairy production, SDCP should facilitate farmers to
        acquire other skills needed to undertake farming as a business. In particular, SDCP training should
        help farmers to see the connection between profitability of dairy enterprise and skills they need to
        sustain the business. Hence this study recommends that SDCP should enhance dairy enterprise
        development and business.

    4. Strengthen market linkages across the dairy value chain.

    5. This survey recommends that SDCP should carry out an in-depth study of milk marketing to
        determine how costs and benefits of the dairy enterprise are shared between various stakeholders
        across the dairy value chain.

    6. To maximize impact of the dairy interventions, SDCP should carry out training needs assessment
        to prioritize the training needs of various stakeholders in the transformation continuum.

    7. SDCP should carry out an in-depth study to assess the impact of HIV/AIDS, environment, gender
        and the youth on the dairy enterprise.

    8. Finally, SDCP should mainstream gender into its operations and interventions to ensure that
        efforts are made to broaden women's equitable participation at all levels of decision-making.




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2      INTRODUCTION
     The Smallholder Dairy Commercialization Programme (SDCP) is funded by the International Fund for
     Agricultural Development (IFAD) with an overall goal of increasing the income of poor rural households that
     depend substantially on production and trade of dairy products for their livelihood. To improve on the
     implementation and assess the current status of the intended Programme beneficiaries, the SDCP
     commissioned FIBEC Limited to carry out a baseline survey in the Programme’s nine districts namely:
     Nakuru; Nyamira; Bomet; Kisii Central; Uasin-Gishu; Lugari; Nandi North; Trans Nzoia; and Bungoma.



2.1         Programme Goal
     The Programme goal is to increase the income of the poor rural households that depend substantially on
     production and trade of dairy products for their livelihood in the 9 Programme districts.



2.2         Programme Purpose
The Programme has a twofold purpose:
       a)     Improving the financial returns of market-oriented production and trade activities by small operators,
              through improved information on market opportunities, increased productivity, cost reduction, value
              adding, and more reliable trade relations;


       b) Enabling more rural households to create employment through and benefit from expanded opportunities
              for market-oriented dairy activities, in particular as a result of strengthened and expanded farmer
              organizations.


2.3         Programme Components
The Programme is supported through the following components namely;
a)      Organization and Enterprise Skills: The objective of the component is to provide Programme
            beneficiaries with the appropriate organizational, managerial and enterprise skills for them to benefit fully
            from market-driven commercialization of milk production, processing, and trading. A participatory and
            inclusive approach is being used to ensure that individuals, existing and new dairy producers, processor
            and trader groups, including co-operative societies are helped to improve their operations on a sound legal
            and business footing.
b)      Technical Support to Smallholder Dairy Producers: This supports a range of measures to strengthen
            smallholder dairy producers’ access to relevant, up-to-date information and techniques necessary for

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         improving their production and increasing productivity. It includes support to improved fodder production
         and management, development and dissemination of extension materials, implementation of better AI
         services in the Programme area and capacity building for dairy groups, as well as technical training which
         will also include measures to counteract negative environmental impact. A key focus be to reduce the cost
         of milk production and increase amount of milk produced and marketed.


c)      Development of the Milk Marketing Chain: This aims to improve the milk marketing chain and
         smallholder dairy operators’ access to it, through support to the development of a Low-Cost Market
         Information System (LCMIS), strengthening of the Dairy Information Centre (DIC) at the Kenya Dairy
         Board (KDB), support for linking smallholder dairy producers to rural finance operators, capacity
         building for milk marketing groups, a school milk Programme and a study on the marketing opportunities
         and constraints presented by poor rural infrastructure.
d)      Support to Policy and Institutions:          IFAD grant assistance is supporting policy and legislative
         development      for    the    animal     feeds    sub-sector,    development      of    a     strategy     for
         commercialization/privatization of Central Artificial Insemination Station (CAIS), harmonization of breed
         services including recording and AI services and a stakeholder validation process. Loan financing will
         support the institutional reform process and policy awareness among farmers on the impact of policy
         issues on their daily activities. Curricular and technical strengthening of the Dairy Training Institute (DTI)
         is planned with grant support for three years of technical assistance. The KDB will also be strengthened
         by the set up and operation of a DIC, linked to the Low-Cost Market Information System (LCMIS)


2.4      Scope of the Assignment
     The baseline survey was required to provide comprehensive information for planning and decision-making
     besides providing benchmarks against which Programme interventions will be assessed. The survey was also
     expected to provide data on and describe the characteristics of the physical, economic and social environment
     in which the target beneficiaries operate. Identification of existing gaps including the comprehensive
     assessment of training needs of the beneficiaries and Programme implementers was a very important
     component of the assignment.




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3     METHODOLOGY
3.1     Overview of the Methodology
In this survey the study team used participatory methodologies involving SDCP staff and key stakeholders. This
section is divided into four components. The first part provides an overview of the data sources used while the
other three parts describe specific methodologies used in getting baseline of each of the three components
namely: levels of production, income levels, farmers groups and quantitative and qualitative indicators for the
future monitoring and evaluation.


The study was carried out in DCA 1 and DCA 3 for the following purposes; DCA 1 to give us a feel of what has
happened after SDCP interventions. DCA 3 was the baseline meant to provide benchmarks for the Monitoring
and Evaluation System. For this to be achieved, the data for DCA 1 and DCA 3 was analyzed separately.


3.2     Area of Coverage
The baseline survey in the Programme’s nine districts namely: Nakuru; Nyamira; Bomet; Kisii Central; Uasin-
Gishu; Lugari; Nandi North; Trans Nzoia; and Bungoma. Table 1 below shows the Dairy Commercialization
Areas (DCAs) covered by the project which are then mapped out in Figure 1 below. The survey was
concentrated in DCA1 where the activities have have been carried out since 2006 and DCA 3 where the
program activities had not started at the time of the survey. The key assumption, made with the concurrence
with the programme management, given the urgent need to generate monitoring indicators in the Logical
Framework, the baseline would provide the basis for monitoring programme outcomes in DCA 3 and to gauge
the progress made in the implementation of DCA1.




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  Table 1: Administrative Areas of DCAs in the Programme Area
DISTRICT                       DCA1                             DCA2                             DCA3
                    Location            Division     Location         Division      Location             Division
                 Sugumerga,           Sigor        Kembu            Longisa      Ndaraweta             Bomet Central
BOMET
                 Sigor
                         Sugumerga                         Kembu                              Ndaraweta
                 Keumbu,       Keumbu              Bogiakumu     Suneka          Bogeka             Mosocho
KISII CENTRAL
                 Ibeno                             Bomorenda                     Etora
                 Kegati                            Bosongo         Kiogoro       Nyakoe
                           Keumbu                     Kiogoro/Bogiakumu                    Mosocho
                 Bonyamatuta      Nyamira          Nyasiongo                     Bonyamatuta
NYAMIRA          Chache                                                          Masaba         Nyamira
                 Bogichora                                          Borabu
                 Keera            Nyamaiya         Makenene                      Ekerenyo              Ekerenyo
                 Kiabonyoru       Ekerenyo
                       Nyamira Peri-uban              Nyasiongo-Mekenene              Ekerenyo-Bonyamatuta
                 Kapsabet        Kapsabet          Sigot           Kosirai       Lolkeringet        Kabiyet
NANDI NORTH      Kipture         Kalibwoni         Kabisaga        Kabiyet       Kabiemit
                       Kapasabet-Kipture                  Sigot-Kabisaga                Lolkeringet-Kabiemit
                 Endebess        Endebess          Kiminini        Kiminini      Waitaluk           Baraka
TRANS NZOIA                Endebess                          Kiminini                         Waitaluk
                 Ndalu            Tongaren         Ndivisi         Ndivisi       Bukembe            Kanduyi
BUNGOMA                     Ndalu                             Ndivisi                        Bukembe
                 Likuyani         Likuyani         Lwandeti        Matete        Lugari             Lugari
LUGARI                                                                           Chekalini
                             Likuyani                       Lwandeti                         Lugari
                 Kapseret          Kapseret        Moi’s Bridge Soy              Sugoi            Turbo
UASIN GISHU                  Kapseret                     Moi’s Bridge                        Sugoi
                 Rongai            Rongai          Ngata          Njoro          Subukia          Subukia
NAKURU           Lenginet                                                        Kabaazi          Kabaazi
                              Rongai                            Ngata                    Subukia/Kabaazi
  Source: SDCP




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Figure 1: Map showing the Area covered by the Survey




Source: Baseline Team (April 2009)
3.3     Design of the study

3.1.1 Sampling
The target sample was 870 heads of dairy households in the project area. This is about 10% of the smallholder
dairy households in DCA1 and DCA 3 whose estimated population is 8,700 households. However, after outliers
were discarded from the analysis, Table 2 below shows the sample population comprised of 321 respondents in
DCA 1 and 463 respondents in DCA 3.
Table 2: Number of Households Interviewed by District and DCA
                        DCAs              Total
 District           DCA 1     DCA 3
 Bomet             45        40        85
 Kisii Central     34         59       93
 Nyamira           38         63       101
 Nandi North       32         44       76
 Trans Nzoia       47         43       90
 Bungoma           27         43       70
 Lugari            19         67       86
 Uasin Gishu       40         50       90
 Nakuru            39         54       93
 Total             321        463      784
Source: Baseline Survey, April, 2009
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While this study’s target was to interview at least 97 households in each district, the study team was unable to
meet this target in Kisii Central because of non-response and incomplete responses coupled with logistical
constraints during the fieldwork. However, the 60 households interviewed is a statistically large sample and
forms the basis forms for the results generated from this survey.


To make projections in each district, the study team used the estimated population in the National Sampling
Frame that is maintained and used by the Kenya National Bureau of Statistics. The Sample Frame was
developed from the 1999 Population and Housing Census and contains 1,133 clusters (of which 930 were rural
and 203 were urban), with each cluster having approximately 100 households. Each household in the cluster is
identified by a number, the name of the household head and the exact village location. There are Cartographic
maps to show the location of each household in the cluster.


Pre-testing: To ensure consistency and collection of high quality data, the team used one day to pre-test the
survey tools in Rongai Division. The data collection in each district was carried out with support from project
staff and SDCP coordinator in each district.


Survey: The field study team comprised of the team leader and one enumerator in each of the nine districts in
the programme area. The field work was carried out between March 20, 2009 and April 3, 2009. This was
because data collection started at the onset of the long rains with the attendant logistical problems. Focus group
discussions and key informant interviews were used to collect qualitative data especially on knowledge and
attitudes of smallholder dairy farmers and milk traders. The consultant in each district worked closely with the
programme officers to organize focus group discussions and identify key informants. Data collected through
key informant interviews and FGDs were analyzed the same day it was collected.

Stakeholder Workshops: The initial results of the Baseline Survey were shared with stakeholders in Nakuru
on August 15, 2009 and Kisumu on September 16, 2009 for their input and suggestions. The inputs from those
workshops were then incorporated in this report.

3.1.2 Methods of Data Analysis and Presentation
The following analyses were carried out on the data:
        Exploratory Analysis – to generate relevant descriptive statistics especially, frequencies, means,
        standard deviations and descriptive statistics.
        Associations and Cross-tabulations using Statistical Programme for Social Sciences (SPSS)
        Estimation of the various indicators in the project logical framework
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During this stage, the following computer software was used: Microsoft Excel – for data management and
Statistical Programme for Social Sciences (SPSS). In addition, key parameters were mapped using Visual Basic
interfaced with ArcGIS. The survey findings were presented using Microsoft PowerPoint incorporating graphs,
maps, tables and photographs.


3.4     Training of Enumerators
The training of enumerators were geared towards sharing a common understanding of the questionnaires and to
polish up their interviewing skills. The training process covered three basic topics: Principles of Interviewing,
Completing the questionnaires and observation techniques on key areas that were used to countercheck the
feedback from the respondents.



3.5     Sources of Data and Collection Techniques
To conduct this survey, the study team collected both quantitative and qualitative data from both primary and
secondary sources. The consultant team used different techniques to collect data in this survey. The data
collection techniques used included: review of secondary data, key informant interviews, focus group
discussions, observations and stakeholder workshops. These techniques included both desk and field studies. A
brief discussion on the techniques, data and information collected is outlined below.


3.6     Secondary Data Sources
The study team collected secondary data in the nine districts from institutions such as the District Livestock
Production Officers in the Ministry of Livestock Development, staff of Kenya Dairy Board (KDB), NGOs
implementing dairy projects such as Heifer Project International, International Livestock Research Institute
(ILRI), TechnoServe, etc delivery records by dairy cooperatives, small and large processors such New KCC,
Brookside Dairies and Spin Knit etc, dairy input suppliers including genetics such as Central Artificial
Insemination (CAIS), ABSTCM, Worldwide Sires and other key stake-holders and interest groups. The lead
agency is the Ministry of Livestock Development (MOLD). The lead agency works in collaboration with the
MOCDM, MOA and the Ministry of Gender, Sports, Culture and Social Services (Department of Social
Services) and other stakeholders.


At the district level, key informants included heads of departments involved in the programme implementation
including District Livestock Production Officers (Coordinating), District Cooperative Officers, District Gender
and Social Development Officers, District Veterinary Officers, KARI Researchers, Processors Representative,
KDB representative and other stakeholders such as KLBO officials where they had offices.
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3.7     Literature Review
The study team identified existing information sources and assembled relevant literature on the dairy farming,
milk trade, processing and marketing within the project area. The team then reviewed recent assessments of the
dairy industry in Kenya. These included: district reports by the Ministry of Livestock Development; impact
assessments of post election violence on the dairy industry by Land O’Lakes etc. Based on findings of the
literature review, the team identified critical information gaps that were in-built into the study tools for further
discussions with key informants.


The study team also reviewed literature on livestock production by the Smallholder Dairy Commercialization
Programme of the documents they reviewed included SCDP project documents, progress reports and other
relevant studies and research findings. In addition to the relevant literature, the study team identified relevant
data-bases to provide further insights on dairy production and performance including Household Surveys in the
nine districts by Kenya Bureau of Statistics and Tegemeo Institute of Policy Analysis.


3.8     Key Informants
To augment information and data from secondary sources, the study team interviewed selected key informants.
These comprised a cross section of individuals across the dairy value chain with firsthand knowledge and
experience on dairy production, bulking and cooling, processing and packaging, transport and distribution of
dairy products. Specifically, key informants were drawn from: KDB, community (farmers, small milk traders,
service providers, input suppliers, and their associations, and relevant government departments etc.


Finally, the study team interviewed key informants in animal feed manufacturing, dairy processors, firms in the
animal health industry, transporters, agro-vet operators, micro-enterprises especially milk bars, shops and kiosks
and dairy cooperatives using discussion guides after identifying gaps from the secondary sources. Some of the
key informants to be interviewed are highlighted below.


  i).   Kenya Dairy Board
Within KDB, key informants were drawn from the senior management in the organization especially regional
managers covering the project area, finance and inspectorate departments. Some of the information and data that
was sought from KDB included: developments in dairy in the respective districts, number of registered milk
traders and small dairy enterprises, the opportunities and constraints encountered in improving milk handling
practices.



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 ii).   Programme Partners
Among the project partners, key informants were drawn from other government departments, local authorities,
and dairy cooperatives, NGOs supporting dairy projects, commercial dairy farmers, community opinion leaders
and small dairy enterprises. Some of the information and data that the study team gathered included: their roles
and involvement in decision making; their relationship with facilitation institutions in the dairy sector and the
community; their capacities in terms of staffing, expertise and physical resources; how they have performed
within the project; their opinion on potential to build a commercially viable smallholder dairy enterprise and
opportunities and constraints for entering and staying in smallholder dairy production and milk marketing and
suggestions to overcome those constraints.


iii).   Government Agencies
Key informants within the Government departments were drawn from the Kenya Dairy Board, Public Health,
Veterinary, Dairy Training Institute and Ministry of Cooperatives. The study team sought information on the
involvement of other agencies in decision making; existing capacities; their opinion on the policies and legal
framework governing dairy production and milk marketing; constraints and weaknesses and suggestions to
redress them.
iv).    Other Stakeholders
Interviews were conducted with other selected key players and interest groups such as Dairy Regulatory
Forums, namely: Land O’Lakes, TechnoServe and Heifer Project International. From these groups, information
and data were sought on their collaboration and relationship with Kenya Dairy Board; their current and
envisaged roles in dairy farming and milk marketing; their opinion on the policies and legal framework guiding
trade of milk and other dairy products in Kenya.


 v).    Market Outlets
Finally, information and data on dairy products and markets were sought from: milk bars, informal milk traders,
hotels and restaurants, animal feed manufacturers, supermarkets, dairy product outlets that emerged in the
course of the study. The information that were gathered include: legal requirements to operate the businesses;
volumes handled, incomes earned, type of dairy products handled, marketing channels, target
markets/consumers, current and expected demand for each product, un-exploited market opportunities, their
participation in dairy; constraints and suggestions to redress those constraints.


3.9     Focus Group Discussions and Key Informant Interviews
The study team conducted one focus group discussion in each of the nine districts with a limited group of milk

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traders and consumers in urban areas with high concentrations of low-income groups. In addition, the team
conducted focus group discussions with management committees of dairy groups.


3.10    Field Visits
The study team comprised local enumerators one from each district in the project area to administer
questionnaires. The enumerators worked closely with the SDCP District and Divisional Coordinators in each of
the nine districts to take advantage of farmer organized forums and training programs that were on-going as part
of the implementation of SDCP. During fieldwork, the study team observed and interacted with dairy farmer
groups, traders, coordination, facilitative and regulatory agencies the dairy value chain.


3.11    Case Studies
To capture breath, depth and context of smallholder dairy farming and milk marketing environment, “in their
own terms about what been significant in their own lives, case studies of both positive and negative deviants
were studied. This information provides better insights into the assessment than pre-conceived questionnaires
and rigid statistical methods. At least two paired interviews and one case study were conducted in each district
after consultations with other stakeholders.
3.12    Photographs
The research team took photographs of milk production under smallholder conditions in the project area, market
outlets and participants as well as infrastructure especially for micro- enterprises such as milk bars and dairy
cooperatives whenever opportunity arises.




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4   STUDY FINDINGS
4.1 Nutritional Status
To determine the nutritional status in each district within the programme area, the study used the
results of the Kenya Integrated Household Budget Survey (KIHBS) that was conducted by Kenya
National Bureau of Statistics in December 2005. The study used some of indicators defined by the
World Health Organization (WHO) and National Centre for Health Statistics (NCHS) to identify
poverty indicators and benchmarks, measure and monitor poverty and living standards and to update
the urban Consumer Price Index (CPI) and establish the rural one.

Three indices notably: Height–for-Age, Weight-for–Age, Weight-for–Height, are used to assess the
nutritional well being of children. This also reflects the economic and social well being of the
population. Nutritional status is determined from the extent to which the indices deviate from the
median/WHO NCHS reference population growth standards. A child falling below -2 standard
deviations (-2 and below -3 standard deviations (-3) is severely malnourished. In the WHO/NCHS
reference population, 2.14% and 0.1% fall below -2SD and -3SD respectively.

Stunting (HAZ) Height–for-Age index measures linear growth. A child falling below -2 standard
deviations from the median of the reference population in terms of height-for-age is considered too
short for his/her age or stunted (chronic malnutrition). A child falling below -3 is severely
malnourished. Underweight (WAZ) weight-for-age is a composite index for weight for height and
height for age and thus does not distinguish between acute malnutrition (wasting) and chronic
malnutrition (stunting). Wasting (WHZ) weight-for-height describes current nutritional status. A child
below -2 is considered to have weight too low for her height or wasted (acute malnutrition)

Percentage of children who are under five from poor and non poor households who are severely or
moderately undernourished

Table 3: Nutrition Status of Children among the Poor and Non-Poor Households in the Project Area
(Poor Households)
Region              Underweight          Stunting         Wasting               Number Of
                                                                                 Children
                    -2SD        -3SD     -2SD    -3SD     -2SD      -3SD
Bomet               11.4      7.6        39.7    22.7     6.2       0.0     27,587
Nakuru              14.3      1.8        67.5    31.5     2.2       0.0     38,895
Nyamira             18.2      5.0        49.4    16.8     0.0       0.0     27,223
Kisii               13.1      6.0        47.6    21.6     2.3       0.0     42,566
Transzoia           16.0      6.9        34.1    18.1     7.4       0.0     29,018
Uasin Gishu         15.5      4.6        37.3    15.9     6.4       0.0     23,353
Nandi               13.0      6.9        28.4    10.4     9.6       3.4     31,453
Bungoma             21.2      2.7        32.3    16.8     4.9       0.0     60,696
Lugari              2.1       0.0        23.1    12.9     0.0       0.0     12,992
Source: KNBS Household Welfare Survey, December 2005


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(Non-Poor Households)
Region       Underweight          Stunting                   Wasting                Number Of
                                                                                    Children
                -2SD       -3SD   -2SD            -3SD       -2SD        -3SD
Bomet           9.2        0.0    46.6            21.9       0.0         0.0        19,952
Nakuru          14.1       2.9    42.3            26.1       2.9         0.0        41,807
Nyamira         17.3       8.1    41.1            21.8       1.2         0.0        43,139
Kisii           7.5        2.6    39.7            19.5       0.0         0.0        26,253
Trans Nzoia     9.1        3.5    39.3            27.1       2.4         0.0        28,336
Uasin Gishu     20.2       3.7    41.9            23.7       2.5         0.0        28,261
Nandi           20.6       2.9    26.2            7.4        19.1        2.9        46,446
Bungoma         21.9       3.1    24.1            13.7       6.0         0.0        72,344
Lugari          3.3        0.0    34.7            9.7        0.0         0.0        17,279
Source: KNBS Household Welfare Survey, December 2005

About one fifth (22.7% and 21.9%) of the children in Bomet district from the poor and non poor
households respectively are severely stunted (too short for their age) when (7.6%) of the children from
the poor households are severely malnourished. The number of stunted children is higher in the non
poor households(46.6%) as compared to the poor households(39.7%). Bomet district has the highest
number of severely malnourished children in the region from the poor households which accounts for
7.6% of the children


In Nakuru District the children of the poor are more likely to be stunted when compared to those of the
non-poor households. The table shows that about 67.5% of the children for the poor houses are stunted
as compared to the non poor who account for 46.6% of all children .The number of severely
malnourished children is quite low which is 1.8% of the children from the poor households and this
number increases in the non-poor households to 2.9% of the children.
The number of severely malnourished children in Nyamira district is higher in non poor households
than in the poor households which accounts for 8.1% and 5.0% respectively. Moreover, the number of
stunted and severely stunted is (49.4% and 16.8%) in the poor households and 41.1% and 21.8% in
the non poor households. while the highest number of malnourished children in the region is from the
non-poor households     in this district which has around 8.1% of its children who are severely
malnourished.




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Kisii district has the highest number            of underweight(13.1%) severely underweight(6.0%),
stunted(47.6%) ,severely stunted(21.6%) and wasted children (2.3%) in the poor households than in
the non poor households which has (7.5%,2.6%,39.7%,19.5%) respectively.


In Trans Nzoia district there are twice as much children who are severely malnourished in the non poor
household(6.9)% than in the poor households(3.5%) . In addition, there are more stunted and severely
stunted children from the non poor households than in the poor households. However, the number of
wasting children is lower in the non poor households (2.4%) as compared to the poor households
(7.4%).


The number of severely malnourished children in Uasin Gishu is from the poor households (4.6%)
which is higher than the number in the non poor households (3.7%). However, the number of stunted
and severely stunted is higher in the non poor households (41.9% &223.7%) than in the poor
households (37.3% &15.9% respectively).


From Table 3 above, Bungoma district has the highest level of prevalence of malnourished children
among the poor , where about one fifth (21.2%) of all children under five are malnourished as
compared to the non poor which has almost the same percentage (21.9)% of malnourished children
under the age of five years. The prevalence for stunted, severely stunted wasting children account for
32.3% and 16.8% and 4.9% in the poor households in contrast to 24.1%, 13.7%, 6.0% in the non
poor households respectively.
Lugari district has a lower prevalence of malnourished (2.1%) and stunted (23.1%( children in poor
households in relation to the non poor households which has a higher prevalence of malnourished
(3.3%) and stunted (34.7%) children respectively. The number of wasting children in poor households
is 12.9% and 9.7% in non poor households


Bomet district has the highest number of severely malnourished children in the region from the poor households
which accounts for 7.6% of the children while the highest number of malnourished children from the non-poor
households are found in Nyamira district which has around 8.1% of its children who are severely malnourished.




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                                                                                           SDCP Baseline Survey Report



4.2   Households
Below are the results of the analysis of households characteristics in the project area in order to place in context
the economic activities that impact dairy producing households in the area covered by the Smallholder Dairy
Commercialization Programme. This section outlines household characteristics namely: household size and
highest level of education of the household head. These are important considerations in small-scale dairy
farming because they help to tailor interventions to match the circumstances of dairy farmers.
4.3   Level of Education
The survey found that 94% of all household heads in SDCP project area are literate as shown in Figure 2 below.
This was expected because households that own dairy cattle are wealthier and therefore more likely to have a
higher level of education than non-dairy households. This is because education opens other income generating
opportunities which otherwise are not available. This finding strongly suggests that SDCP can use written
messages to communicate to the target groups.
Figure 2: Education Level of Dairy Farmers




Source: Analysis of the Baseline Survey, April 2009

There is a large variation between the literacy levels in each district with Lugari, Nakuru and Bomet
districts having the highest proportion of college educated household heads while Kisii Central has the
lowest number. This is shown in Table 4 below.




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Table 4: Highest Education level of household heads by District and DCA
                              Highest educational level attained
                                       Primary            Secondary College/
      District              None       education          education University   Total
 DCA1 Bomet                    4.4%              55.6%        26.7%     13.3%    100.0%
      Kisii Central            0.0%              23.5%        70.6%      5.9%    100.0%
      Nyamira                  2.6%              28.9%        63.2%      5.3%    100.0%
      Nandi North              0.0%              53.1%        37.5%      9.4%    100.0%
      Trans Nzoia              2.1%              70.2%        27.7%      0.0%    100.0%
      Bungoma                  3.7%              33.3%        51.9%     11.1%    100.0%
      Lugari                  10.5%              26.3%        36.8%     26.3%    100.0%
      Uasin Gishu              7.5%              52.5%        32.5%      7.5%    100.0%
      Nakuru                   2.6%               7.7%        74.4%     15.4%    100.0%
      Total                    3.4%              41.1%        46.1%      9.3%    100.0%
 DCA3 Bomet                    7.5%              35.0%        40.0%     17.5%    100.0%
      Kisii Central           30.5%              28.8%        35.6%      5.1%    100.0%
      Nyamira                  0.0%              14.3%        77.8%      7.9%    100.0%
      Nandi North              4.5%              52.3%        34.1%      9.1%    100.0%
      Trans Nzoia              2.3%              27.9%        46.5%     23.3%    100.0%
      Bungoma                 14.0%              37.2%        41.9%      7.0%    100.0%
      Lugari                   1.5%              19.4%        52.2%     26.9%    100.0%
      Uasin Gishu              6.0%              44.0%        38.0%     12.0%    100.0%
      Nakuru                   9.3%              18.5%        50.0%     22.2%    100.0%
      Total                    8.4%              29.4%        47.5%     14.7%    100.0%
Source: Analysis of the Baseline Survey, April 2009


This analysis shows that Kisii Central has the largest proportion of illiterate dairy farmers while Nyamira has
the least in DCA 3. This suggests that visual materials and the radio would be the better mediums to
communicate the extension messages in this district. However, under the current social structures, the impact of
illiterate household heads on technology uptake is often compensated by other literate members of the
household.


4.4    Household Size
This survey showed that average household in DCA 1 had 6.76 members compared to 6.75 members in DCA 3
as shown in Table 5 below. The standard deviation however suggests that there is no significant difference
between the size of households in DCA 1 and DCA 3.




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                                                                                         SDCP Baseline Survey Report




Table 5: Size of household by District and DCA
District         DCA Area         Mean       Std. Deviation
Bomet            DCA1                 7.12           2.455
                 DCA3                 7.73           3.252
Kisii Central    DCA1                 5.97           2.455
                 DCA3                 6.42           1.749
Nyamira          DCA1                 6.79           2.029
                 DCA3                 6.08           1.753
Nandi North      DCA1                 6.17           2.135
                 DCA3                 5.50           2.585
Trans Nzoia      DCA1                 7.08           3.676
                 DCA3                 7.51           3.245
Bungoma          DCA1                 7.97           4.231
                 DCA3                 7.89           3.325
Lugari           DCA1                 6.86           2.007
                 DCA3                 7.34           2.478
Uasin Gishu      DCA1                 7.27           4.981
                 DCA3                 6.64           2.795
Nakuru           DCA1                 5.44           1.832
                 DCA3                 5.98           1.995
Total            DCA1                 6.76           3.179
                 DCA3                 6.75           2.671
                 Total                6.76           2.891
Source: Baseline Survey, April 2009


The survey showed that the largest households were in Bungoma, Bomet, Trans Nzoia and Lugari Districts. The
average household in the program area has 6.79 members which suggest that a large number of households use
family labour in the enterprise and they have equally high on-farm milk consumption.


4.5        Main Occupation of Household Head
This survey found that 31.2% of the farmers in DCA 1 considered dairy farming as their primary source of
income as shown in Figure 3 below. It is significant that 26% of the farmers considered subsistent farming as
their main occupation and therefore dairy was one of the miscellaneous income sources. The survey findings
suggest that lucrative returns from dairy farming is attracting individuals that are involved in other occupations
such as business people and salaried workers who comprised 12% and 9% of the dairy farmers in the program
area respectively.


In DCA 3, the proportion of farmers whose primary source of income was dairy farming was 29.6% as shown in
Figure 4 below. Commercial farming refers to large scale crop farming – especially wheat and maize.

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                                                                             SDCP Baseline Survey Report




Figure 3: Main Occupation of Household Head in DCA 1




Source: Baseline Survey, April 2009


Figure 4: Main Occupation of Household Heads in DCA 3




Source: Baseline Survey, April 2009

Table 6 below shows that 20% of the households in DCA 1 relied on commercial crop farming.
Farmers in Trans Nzoia (61%) and Nyamira (55%) Districts accounted for the largest proportion of
commercial crop farmers. The largest proportion of farmers who relied on dairy farming as the main
source of income in DCA 1 were in Bungoma (90%) and Nandi North (74%) district. This strongly



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suggests that there could be a change in attitude in DCA 1 where SDCP has been working for last two
to three years rather than a significant shift in the main sources of income.
Table 6: Main Occupation of the Household Head by District in DCA 1
          District        Business Salaried       Dairy       Commercial Subsistence Mixed   Casual
                                    employment farming farming           farming     farming labourer
DCA1      Bomet                16%          12%        2%            2%         69%       0%                      0%
          Kisii Central         6%           9%       21%            3%         12%      50%                      0%
          Nyamira               3%           8%       21%           55%         13%       0%                      0%
          Nandi North           9%           6%       74%           11%          0%       0%                      0%
          Trans Nzoia           2%           4%       31%           61%          2%       0%                      0%
          Bungoma               3%           0%       90%            3%          3%       0%                      0%
          Lugari               10%          19%        5%           24%         38%       0%                      5%
          Uasin Gishu          18%           7%       39%            9%         27%       0%                      0%
          Nakuru                5%          21%       13%            3%         59%       0%                      0%
           Total                8%           9%       32%           20%         26%       5%                      0%
Source: Baseline Survey, April 2009

Table 7 below shows that 87% of the farmers in Bungoma District relied on dairy farming as the
primary source of income in DCA 3 compared to only 5% of the farmers in Nakuru District. The other
important sources of income for farmers in DCA 3 were subsistence farming and business.
Table 7: Main Occupation of the Household Head by District in DCA 3
District        Business     Salaried     Dairy  Commercial Subsistence Mixed   Casual
                             employment farming farming     farming     farming labourer
Bomet                 2%               2%    32%       14%         50%      0%                      0%
Kisii Central        32%               8%    43%        7%          0%      8%                      2%
Nyamira              29%               2%    11%       57%          2%      0%                      0%
Nandi North          15%              10%    48%       15%         10%      2%                      0%
Trans Nzoia           9%               4%    38%       20%         29%      0%                      0%
Bungoma               4%               2%    87%        2%          2%      2%                      0%
Lugari               10%              20%    10%        3%         57%      0%                      0%
Uasin Gishu          15%               9%    23%        9%         43%      0%                      0%
Nakuru               11%              18%     5%        4%         62%      0%                      0%
Total                15%               9%    31%       15%         29%      1%                      0%
Source: Baseline Survey, April 2009

4.6    Land Size
Analysis of the land holding across the Dairy Commercialization Areas (DCAs) showed that on average, DCA 3
had farmers with a larger land holding compared to DCA 1 as shown in Table 8 below. This confirms that DCA
1 and DCA 3 started from slightly different availability of land resource.

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Table 8: How much land is available to this family?
 District            N      Mean        Std.
                                        Deviation
 Bomet                 45         4.4          2.6
 Kisii Central         34         2.3          2.0
 Nyamira               38         2.4          1.6
 Nandi North           34         3.2          3.3
 Trans Nzoia           49         4.2          4.3
 Bungoma               29         5.1          4.7
 Lugari                19         5.0          4.3
 Uasin Gishu           42         4.1          3.9
 Nakuru                39         2.1          1.9
 Total                329         3.6          3.4
 Bomet                 42         4.4           2.4
 Kisii Central         60         4.5           2.5
 Nyamira               63         2.2           1.3
 Nandi North           45         4.6           3.7
 Trans Nzoia           45         3.7           4.3
 Bungoma               43         3.6           2.8
 Lugari                68         4.3           3.9
 Uasin Gishu           52         5.4           3.9
 Nakuru                54         2.4           2.2
 Total                472         3.9           3.3
Source: Baseline Survey, April 2009

4.7   Land Ownership
The study showed that only 8% of the smallholder dairy farmers did not own land in DCA 1. It further shows
that Kisii Central District had the highest proportion (24%) of dairy farmers who did not own land followed by
Lugari (21%) as shown in Table 9 below. These landless dairy farmers were relying on communal land to graze
their animals or were renting land.




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Table 9: Land Ownership by District in DCA 1
            Do you own this farm?
 District      Yes       No          Total
Bomet              84%        16%       100%
Kisii Central      76%        24%       100%
Nyamira           100%         0%       100%
Nandi North        97%         3%       100%
Trans Nzoia        98%         2%       100%
Bungoma            97%         3%       100%
Lugari             79%        21%       100%
Uasin Gishu        98%         2%       100%
Nakuru             90%        10%       100%
 Total             92%         8%       100%
Source: Survey, April 2009


However, in DCA 3, the proportion of dairy farmers who did not own the land on which they were undertaking
the activities was 9% as shown in Table 10 below. Bungoma, Lugari and Uasin Gishu Districts contributed the
largest proportion of landless dairy farmers. All the dairy farmers in DCA 3 from Trans Nzoia District
responded that they were land owners.


Table 10: Land Ownership by District in DCA 3
             Do you own this farm?
  District      Yes       No          Total
 Bomet              98%         2%      100%
 Kisii Central      90%        10%      100%
 Nyamira            97%         3%      100%
 Nandi North        98%         2%      100%
 Trans Nzoia       100%         0%      100%
 Bungoma            79%        21%      100%
 Lugari             84%        16%      100%
 Uasin Gishu        85%        15%      100%
 Nakuru             89%        11%      100%
  Total             91%         9%      100%
Source: Survey, April 2009

The study team sought to understand the circumstances of farmers who did not own the land on which they were
carrying out the dairy activities. Table 11 below shows that 25% of the dairy farmers who did not own land in
DCA 1 were tenants who were largely in Bungoma, Nakuru and Trans Nzoia districts. The major constraint in
land use for tenants was that they could not carry out permanent or long term land developments such as soil

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                                                                                       SDCP Baseline Survey Report



conservation structures or pasture development. These dairy farmers were also renting land from other farmers
to supplement their small holdings.


Another 39% (most of them in DCA 3) were using communal land and therefore did not have any incentive to
develop the land they were using because they could not restrict the use from other members of the community.
In other situations, dairy farmers were exploiting family land which had similar restrictions as communal land
and in other cases, dairy farmers did not have title deeds to the land that they were using which reduced their
incentive for long term investment.

Table 11: Circumstances of dairy farmers who did not own land
 DCA Area      Reasons why you do not own land?                            Total
                                                                Have
                                         Communal               no title
               District         Tenant   land use   Family      deed
 DCA1          Bomet                0%          13%    88%           0%     100%
               Kisii Central       13%           0%    88%           0%     100%
               Nandi North          0%           0%   100%           0%     100%
               Trans Nzoia        100%           0%     0%           0%     100%
               Bungoma            100%           0%     0%           0%     100%
               Lugari               0%          75%    25%           0%     100%
               Uasin Gishu          0%        100%      0%           0%     100%
               Nakuru             100%           0%     0%           0%     100%
                Total              25%          18%    57%           0%     100%
                                                                Have
                                         Communal               no title
 DCA3          District         Tenant   land use   Family      deed       Total
               Bomet                0%           0%   100%           0%     100%
               Kisii Central       50%          50%     0%           0%     100%
               Nyamira             50%          50%     0%           0%     100%
               Nandi North        100%           0%     0%           0%     100%
               Bungoma            100%           0%     0%           0%     100%
               Lugari               0%          82%     0%          18%     100%
               Uasin Gishu         13%          75%    13%           0%     100%
               Nakuru              83%          17%     0%           0%     100%
               Total               45%          45%     5%           5%     100%
Source: Baseline Survey, April 2009

In DCA 3, Nandi North and Bungoma Districts had the highest number of tenants’ dairy farmers.

4.8   Land Use

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The average land holding in the programme area is 4.25 acres of which 50% is used for crop cultivation, 30%
for pastures and only 11% for fodder as shown in Figure 5 below.

Figure 5: Land use




Source: Baseline Survey, April 2009

On further analysis of the land use as shown in Table 12 below, Lugari District emerged as the district with the
largest average land holding of 5.08 acres followed by Trans Nzoia District 5.05 acres, while Nyamira District
has the average land size of 2.0 acres per household. These findings are consistent with the choices made by
dairy farmers in terms of the number of dairy animals that they keep given this land available. These results
also suggest that there is very little scope for increasing herd density under the current production system
without widespread adoption of zero grazing technology in the programme area.

Table 12: Land use in DCA1 by District
                                                                      Total
DCA Area District      Fodder    Pasture   Crops Buildings   Others
DCA1     Bomet         0.30      2.00      1.80 0.06         0.17     4.33
         Kisii Central 0.76      0.18      1.03 0.29         0.03     2.29
         Nyamira       0.75      0.19      0.97 0.00         0.03     1.94
         Nandi North 0.20        0.97      1.20 0.15         0.11     2.63
         Trans Nzoia 0.30        1.23      2.10 0.21         0.20     4.04
         Bungoma 0.55            0.63      2.83 0.00         0.00     4.01
         Lugari        0.76      0.90      2.89 0.37         0.16     5.08
         Uasin Gishu 0.29        2.16      1.17 0.07         0.10     3.79
         Nakuru        0.33      0.19      1.04 0.21         0.13     1.90
         Total         0.44      1.00      1.60 0.14         0.11     3.29
Source: Baseline Survey, April 2009


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                                                                                    SDCP Baseline Survey Report



Table 13 below compares land use between the districts in DCA 3 in the programme area.
Table 13: Land use in DCA 3 by District
DCA Area District        Fodder   Pasture   Crops   Buildings   Others   Total
DCA3     Bomet           0.49     1.38      2.38    0.13        0.07     4.45
         Kisii Central   0.87     1.28      2.13    2.34        0.03     6.65
         Nyamira         0.57     0.11      1.36    0.00        0.00     2.04
         Nandi North     0.26     1.46      1.98    0.23        0.19     4.12
         Trans Nzoia     0.46     0.94      1.78    0.10        0.38     3.66
         Bungoma         0.60     0.20      1.82    0.00        0.00     2.62
         Lugari          0.43     0.82      2.61    0.15        0.14     4.15
         Uasin Gishu     0.40     2.20      2.28    0.14        0.10     5.12
         Nakuru          0.32     0.22      1.44    0.16        0.17     2.31
         Total           0.50     0.94      1.98    0.40        0.11     3.93
Source: Baseline Survey, April 2009



4.9   Milking Herd
This study showed that DCA 3 had a higher herd size compared to DCA 1. Dairy farmers in DCA 3 in Lugari
District on average had five milking cows compared to two in Nyamira and Uasin Gishu District as shown in
Figure 6 below.

Figure 6: Average herd Size by District in the project area




Source: Baseline Survey, April 2009




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Table 14: Average size of the milking herd by breed by District in DCA 1
          District        Friesian     Jersey    Guernsey Crossbreed Local           Total
                          cows in      cows in cows in         cows in      cows in
                          milk         milk      milk          milk         milk
DCA1 Bomet                        0.1      0.31            0           0.71    0.37      1.5
          Kisii Central         0.71          0        0.03            0.68    0.15      1.6
          Nyamira               0.39       0.17        0.06            0.28    0.11      1.0
          Nandi North               0         0            0           2.14        0     2.1
          Trans Nzoia           0.06          0            0            1.4        0     1.5
          Bungoma               0.43          0        0.27            0.38    0.22      1.3
          Lugari                1.73          0            0           0.41    0.14      2.3
          Uasin Gishu           0.64       0.02            0           0.47    0.06      1.2
          Nakuru                1.63          0        0.05            0.05        0     1.7
          Total                 5.69       0.50        0.41            6.52    1.05     14.2
Source: Baseline Survey, April 2009


Table 15: Average size of the milking herd by breed by District in DCA 3
          District        Friesian     Jersey    Guernsey Crossbreed Local           Total
                          cows in      cows in cows in         cows in      cows in
                          milk         milk      milk          milk         milk
DCA3 Bomet                      0.76          0        0.04            0.74        0  1.54
          Kisii Central         0.65        0.1        0.08            0.22    0.25    1.3
          Nyamira               0.06        0.1        0.12            1.01    0.11    1.4
          Nandi North           0.09          0            0           1.06    0.15    1.3
          Trans Nzoia           0.33       0.11        0.04            0.67    0.04   1.19
          Bungoma                 0.2       0.1            0           0.44    0.44   1.18
          Lugari                0.40       0.01         0.1            0.62      0.1  1.23
          Uasin Gishu           1.00       0.02        0.02            0.58    0.16   1.78
          Nakuru                1.32          0        0.02            0.08        0  1.42
Source: Baseline Survey, April 2009

4.10 Milk Production
The survey found that out of the 795 respondents, it was only 92% who were producing milk. The average milk
production in DCA 1 was 8.5 litres and 9.1 litres in DCA 3 with standard deviation of 6.81 litres and 6.39 litres
respectively. Table 16 below shows the total milking herd, types of dairy cows in the sample and the total milk
production. It shows that the total daily milk production among the734 households was 6,890 litres. However,
we could not disaggregate the daily milk production by type of animal using the data collection tools because at
the farm level, milk was combined regardless of animal breeds and there were severe time constraint available
to collect all the information in this survey.

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                                                                                        SDCP Baseline Survey Report




However, the distribution of milk production across the programme area was highly skewed with about 70% of
the farmers producing less than 10 litres per day. Uasin Gishu District had the highest milk average production
per farmer registering 14.0 litres whereas Bomet had the least milk production of 6.8 litres as shown in Table 16
below.
Table 16: Average milk production of the dairy herd in litres/day by District
                               DCA1                           DCA3
  District          Mean         Std. Deviation Mean Std. Deviation
  Bomet             3.9          2.98              9.1       3.97
  Kisii Central     10.0         6.36              10.5      6.44
  Nyamira           7.2          6.77              7.7       4.46
  Nandi North       7.0          6.06              10.3      5.60
  Trans Nzoia       7.9          5.22              6.3       4.80
  Bungoma           9.4          5.47              5.2       3.73
  Lugari            14.2         7.90              9.6       5.99
  Uasin Gishu       7.3          5.65              14.0      9.45
  Nakuru            13.3         9.06              9.1       6.41
  Total             8.5          6.81              9.2       6.39
Source: Baseline Survey, April 2009

When distribution of milk production across the project area was mapped out, as shown in Figure 7 below, it
confirmed that SDCP was targeting small scale farmers in both DCA 1 and DCA 3 and that there were small
pockets of high production amid the large numbers of the small-holder production. This finding is consistent
with the project goals.




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                                                                                       SDCP Baseline Survey Report


Figure 7: Distribution of milk production across the SDCP Area




Source: Baseline Survey, April 2009

4.11 Farm Records
This survey found that 39% of the farmers in DCA 3 kept records compared to only 24% in DCA 1 as shown in
Figure 17 below. This is significant difference that cannot be explained by the SDCP interventions. It suggests
that the environment in DCA 3 may be promoting record keeping such as formal market markets.




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                                                                                     SDCP Baseline Survey Report



Table 17: Proportion of households keeping Farm Records in DCA1 and DCA 3
  Proportion of farmers that kept farm
 records
  District        DCA 1 DCA 3         Total
 Bomet              12%        46%      28%
 Kisii Central       0%        77%      49%
 Nyamira             6%         8%       6%
 Nandi North         6%        29%      19%
 Trans Nzoia        20%        37%      28%
 Bungoma            76%        29%      51%
 Lugari             52%        33%      37%
 Uasin Gishu        30%        26%      28%
 Nakuru             66%        68%      67%
 Total              24%        39%      32%
Source: Baseline Survey, 2009


This survey found that only 32% of the farmers in the programme area kept farm records with Kisii Central
(77%) having the highest proportion followed by Nakuru District (67%). Nyamira District had the lowest
adoption rate of 6% as outlined in Table 17 below.

The most common records that farmers kept were production records because farmers delivered milk on credit
and therefore needed to have records to support their claims. Breeding records were the second most important
records that farmers kept in both DCA 1 and DCA 3 as shown in Table 17 below while leasing records are the
least common records.


Table 18 further shows that among the farmers that kept records, 81% of the farmers in DCA 1 kept milk
production record compared to 73% in DCA 3. It also shows that 10% of the farmers in DCA 1 kept breeding
records compared to 13% in DCA 3. The other records namely health and sales were less common. The choice
of the records that farmers kept appear to be market driven.




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                                                                                      SDCP Baseline Survey Report



Table 18: Type of farm records kept by farmers in DCA 1 and DCA 3 by District
                           Milk                                      Leasing
            District       Production Breeding Health Sales          records      Total
            Bomet                 75%        25%        0%     0%            0%    100%
 DCA1
            Kisii Central         88%        13%        0%     0%            0%    100%
            Nyamira              100%          0%       0%     0%            0%    100%
            Nandi North             0%       50%      50%      0%            0%    100%
            Trans Nzoia           78%        11%      11%      0%            0%    100%
            Bungoma               90%          5%       0%     5%            0%    100%
            Lugari                64%          9%     18%      0%            9%    100%
            Uasin Gishu           83%        17%        0%     0%            0%    100%
            Nakuru                78%          7%     15%      0%            0%    100%
            Total                 81%        10%        7%     1%            1%    100%
            Bomet                 86%        14%        0%     0%            0%    100%
 DCA3
            Kisii Central         87%          9%       2%     2%            0%    100%
            Nyamira               80%          0%     20%      0%            0%    100%
            Nandi North           54%        46%        0%     0%            0%    100%
            Trans Nzoia           79%        14%        7%     0%            0%    100%
            Bungoma               46%        31%      15%      8%            0%    100%
            Lugari                55%          5%     20%     20%            0%    100%
            Uasin Gishu           71%          7%       7%    14%            0%    100%
            Nakuru                71%          6%       9%    14%            0%    100%
            Total                 73%        13%        7%     7%            0%    100%
Source: Baseline Survey, April 2009



4.12 Household Welfare
The study found that the average monthly household expenditure in the project area was Kshs 21,423 per month
with education and food expenses accounting for almost 80% of the expenses as shown in Figure 8 below.
These findings suggest that dairy farming households in the project area can only invest less than 5% of their
monthly expenses towards improving the dairy herd because of the education, food, health and transport related
expenses may not be flexible.




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Figure 8: Mean Monthly Household Expenditure

                  Mean Monthly Household Expenditure
                             Kshs 21, 423
                                      Transport Others
                                         6%      5%
                                                  Health
                                                   10%


                         Education
                           44%


                                          Food
                                          35%




Source: Baseline Survey, April 2009


Using the expenditure as the proxy for income, these findings showed that farmers in DCA 3 were spending an
average of Kshs 23,642 per month which suggests they were slightly better off than farmers in DCA 1 who were
spending an average of Kshs 20,847 per month. However, within the project area, there is wide disparity in the
monthly expenditure across the districts as shown in Table 19 below. For instance, dairy farmers in DCA 1 in
Nandi North seem to have the least income averaging Kshs 6,900 compared to their counterparts in Bungoma
district who were spending about Kshs 35,898 per month. Equally notable was that incomes of farmers varied
considerably depending on their occupation and location. Based on this parameter, salaried employees who were
dairy farmers in Trans Nzoia District appear to have the highest income of Kshs 62,900 per month whereas
dairy farmers in Kisii District had the lowest income of only Kshs 3,500 per month.




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Table 19: Household Monthly Expenditure by Type, Occupation and Districts in DCA 1
              Average Household Expenditure by Type, Occupation, District in DCA 1
District     Occupation           Food       Health    Education Transport Others     Total

Bomet      Business              5,071    151          16,180     714      429        22,546
           Salaried employment   5,925    825          3,925     925       575        12,175
           Commercial farming    5,000    3,000        -         -         2,500      10,500
           Subsistence farming   7,203    1,002        3,265     383       245        12,098
           Total                 6,698    896          5,305     476       356        13,731
Kisii      Business              6,500    250          12,500    1,750     -          21,000
Central
           Salaried employment    8,667   2,667        20,000    667       100        32,100
           Dairy farming         4,729    1,043        4,357     1,057     2,600      13,786
           Commercial farming    3,000     200         200       100       -          3,500
           Subsistence farming
                                 5,750    1,900        6,725     1,225     2,625      18,225
           Mixed farming         7,076    1,882        15,472    2,650     94          27,175
           Total                 6,424     1,635        11,930   1,851     900        22,740
Nyamira    Business              8,000    5,000        10,000    2,000     -          25,000
           Salaried employment   5,000    1,000        3,000     4,000     -          13,000
           Dairy farming         10,013   1,650        19,313    5,100     -          36,075
           Commercial farming    6,965    2,395        14,955    1,730     -          26,045
           Subsistence farming   5,900    7,200        10,240    3,000     -          26,340
           Total                 7,349    2,841        14,157    2,822     -          27,168
Nandi      Business              1,000    -            2,000     1,200     -          4,200
North
           Salaried employment   4,000    -            2,500     1,050     -          7,550
           Dairy farming          3,239   52           2,820     583       -          6,693
           Commercial farming     4,000   375          3,875     300       -          8,550
           Total                 3,317    90           2,912     597       -          6,915
Trans      Business               6,000   1,000        -         1,000     -          8,000
Nzoia
           Salaried employment   15,000   1,600        40,000     4,200    2,100      62,900
           Dairy farming         5,933    645          3,263     931       587        11,359
           Commercial farming    6,771    1,047        7,447     1,426     507        17,198
           Subsistence farming   4,500    400          1,833     -         -          6,733
           Total                 6,614    916          6,526     1,287     545        15,888
Bungoma    Business              3,000    1,000        -         800       -          4,800
           Dairy farming         7,771    4,840        21,363    3,779     13         37,766
           Commercial farming    2,500    3,000        25,000    2,000     -          32,500
           Subsistence farming   350      3,000        7,222     15,000    -          25,572
           Total                 7,124    4,562        20,183    4,019     11         35,898

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               Average Household Expenditure by Type, Occupation, District in DCA 1
District      Occupation          Food     Health     Education Transport Others                 Total

Lugari        Business                4,500     200          800         2,350        1,650      9,500
              Salaried employment     13,000    1,100        3,925       2,000        5,000      25,025
              Commercial farming      7,625     350          8,500       4,850        7,000      28,325
              Subsistence farming     9,286     371          20,714      3,471        9,143      42,986
              Casual labourer         1,000     300          7,000       -            3,000      11,300
              Total                   8,750     506          11,294      3,133        6,572      30,256
Uasin         Business                3,457     371          1,914       671          2,143      8,557
Gishu
              Salaried employment     6,000     500          1,250       2,000        1,500      11,250
              Dairy farming           10,567    2,463        4,687       1,955        863        20,535
              Commercial farming      11,625    3,125        4,000       2,300        250        21,300
              Subsistence farming     20,745    8,455        7,209       1,036        2,382      39,827
              Total                   12,036    3,745        4,654       1,503        1,491      23,429
Nakuru        Business                6,000     2,000        4,500       5,000        2,000      19,500
              Salaried employment
                                      6,500     2,375        6,500       4,375        2,750      22,500
              Dairy farming           4,500     2,250        2,842       1,750        1,750      13,092
              Commercial farming      4,000     3,000        2,000       3,000        1,500      13,500
              Subsistence farming     5,200     1,750        6,000       3,100        1,950      18,000
              Total                   5,429     2,000        5,553       3,343        2,100      18,425
Source: Baseline Survey, April 2009

Using expenditure as the proxy for income, these findings showed that dairy farmers in DCA 3 in Nandi North
seem to have the least income averaging Kshs 10,000 compared to their counterparts in Trans Nzoia district who
were spending about Kshs 40,810 per month. Equally notable was that incomes of farmers varied considerably
depending on their occupation and location. Based on this parameter, subsistence farmers in Nyamira District
had the lowest income of only Kshs 4,600 per month while dairy farmers in Lugari District appear to have the
highest income of Kshs 52,900 per month. Generally however, the incomes of farmers in DCA 3 appear to be
higher than those of farmers in DCA 1.




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Table 20: Household Expenditure by source of Income in DCA 3 by District and by Type
              Average Household Expenditure by Type, Occupation, District in DCA3
District     Occupation       Food      Health    Education    Transport Others                      Total
Bomet        Business               2,500       200         1,000          200         -             3,900
             Salaried               8,000       5,000       30,000         4,000       -             47,000
             employment
             Dairy farming          7,462       1,100       3,254          1,077       346           13,238
             Commercial             7,250       485         9,300          1,625       1,025         19,685
             farming
             Subsistence            8,097       1,066       13,009         714         664           23,550
             farming
             Total                  7,664       1,096       9,592          992         564           19,907
Kisii        Business               4,281       6,094       16,031         1,381       41            27,828
Central
             Salaried               3,260       2,140       14,200         1,400       120           21,120
             employment
             Dairy farming          3,652       6,287       12,204         1,007       74            23,224
             Commercial             2,875       -           5,375          975         200           9,425
             farming
             Mixed farming          4,000       400         3,410          780         -             8,590
             Casual labourer        2,000       -           -              250         -             2,250
             Total                  3,746       4,719       11,977         1,117       69            21,628
Nyamira      Business               6,167       378         3,536          1,122       -             11,203
             Salaried               7,000       -           5,000          1,500       -             13,500
             employment
             Dairy farming          8,857       2,114       4,743          1,336       -             17,050
             Commercial             6,889       931         3,326          1,397       -             12,543
             farming
             Subsistence            4,000       -           500            100         -             4,600
             farming
             Total                  6,856       874         3,528          1,291       -             12,550
Nandi        Business               2,857       240         717            829         -             4,642
North
             Salaried               9,600       500         11,000         2,600       -             23,700
             employment
             Dairy farming          3,853       206         3,615          518         -             8,191
             Commercial             3,929       500         5,131          1,157       -             10,717
             farming
             Subsistence            5,400       130         1,266          1,040       -             7,836
             farming
             Mixed farming          6,000       5,000       5,000          1,000       -             17,000
             Total                  4,619       401         4,017          998         -             10,034
Trans        Business               10,875      2,800       19,750         15,088      12,500        61,013
Nzoia
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            Average Household Expenditure by Type, Occupation, District in DCA3
District   Occupation       Food      Health    Education    Transport Others         Total
           Salaried          15,000   1,100     5,500       1,000       500           23,100
           employment
           Dairy farming     9,000    1,507     29,520      1,993       667           42,687
           Commercial        9,889    722       19,611      1,539       522           32,283
           farming
           Subsistence       9,283    4,983     17,667      6,958       2,183         41,075
           farming
           Total             9,736    2,436     21,936      4,514        2,188        40,810
Bungoma    Business           5,500   1,500      6,500      650           -            14,150
           Salaried          6,000    3,000     12,000      2,500       -             23,500
           employment
           Dairy farming     7,181    2,686     5,176       1,608       0             16,651
           Commercial        7,000    4,000     500         2,000       -             13,500
           farming
           Subsistence       6,000    8,000     13,333      450         -             27,783
           farming
           Mixed farming     12,000   1,500     7,667       2,500       -             23,667
           Total             7,156    2,765     5,535       1,587       0             17,043
Lugari     Business          9,014    1,257     4,786       2,443       3,071         20,571
           Salaried          17,385   4,494      20,546     5,500       4,692         52,617
           employment
           Dairy farming     35,000   3,033     8,833       3,867       2,167         52,900
           Commercial        15,000   5,000     18,000      2,000       10,000        50,000
           farming
           Subsistence       8,113    2,486     16,103      1,573       2,543         30,816
           farming
           Total             12,516   2,834     15,160      2,637       3,093         36,240
Uasin      Business          6,725    3,063     3,163       4,263       2,825         20,038
Gishu
           Salaried          5,750    2,000     7,700       600         450           16,500
           employment
           Dairy farming     8,055    1,645     25,109      1,868       1,627         38,305
           Commercial        5,025    1,950     3,500       2,950       750           14,175
           farming
           Subsistence       7,286    1,614     6,362       2,629       2,810         20,700
           farming
           Total             7,052    1,923     9,998       2,584       2,173         23,730
Nakuru     Business          7,400    3,200     5,800       3,400       1,000         20,800
           Salaried          6,875    2,000     7,250       5,313       2,100         23,538
           employment
           Dairy farming     8,000    3,667     5,000       4,333       2,667         23,667
           Commercial        5,000    10,000    2,000       8,000       2,000         27,000
           farming

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             Average Household Expenditure by Type, Occupation, District in DCA3
District    Occupation       Food      Health    Education    Transport Others                Total
            Subsistence           5,467    2,367      6,567         2,967         1,733       19,100
            farming
            Total                 6,064    2,638      6,404         3,606         1,783       20,496
Source: Baseline Survey, April 2009
This analysis shows that dairy farmers in Nandi North incur the least monthly expenses in all categories of
expenditure averaging Kshs 6,915 while Trans Nzoia at Kshs 40,810 had the highest cost of living in DCA 3.
This point is further confirmed by Figure 9 below which maps the mean household expenditure across the
programme area.
Figure 9: Map showing the Mean Household Expenditure




Source: Baseline Survey, April, 2009



On average, this survey suggests that farmers in DCA 1 spent almost twice the amount of money buying water
as in DCA 3 as shown in Table 20 below. However, the standard deviation of these expenses suggests that the


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cost of water is actually insignificant meaning that the respondent in Trans Nzoia who reported spending Kshs
6,000 per day was an outlier.

Table 21: Cost of water in Kshs per day between DCA 1 and DCA 3
 District        DCA1 DCA3
 Bomet             0.6  3.5
 Kisii Central    40.9 134
 Nyamira           5.3  2.5
 Nandi North         0    0
 Trans Nzoia      180 23.1
 Bungoma          0.04  0.5
 Lugari              0 24.8
 Uasin Gishu      150 42.8
 Nakuru           50.4 36.3
 Total            56.2 32.4
Source: Baseline Survey, April 2009


Table 22 below shows the daily cost of getting water in each district which is a critical nutritional input.

Table 22: Cost of water in Kshs per day
 District        Minimum      Maximum         Mean      Std. Deviation
 Bomet                  0          100           1.77          11.093
 Kisii Central            0           1,700      136           262.878
 Nyamira                  0            150       3.56           17.753
 Nandi North              0              0       0.00             0.000
 Trans Nzoia              0           6,000    164.12          860.658
 Bungoma                  0             20       0.29             2.274
 Lugari                   0            600      19.89           92.310
 Uasin Gishu              0           3,000     94.23          370.720
 Nakuru                   0            500      43.88           57.412
 Total                    0         6,000       49.89          340.017
Source: Baseline Survey, April 2009



Dairy farmers incurred the highest cost to access water in Kisii Central District where it costs Kshs 136 but least
in Bungoma where it is Kshs 0.30 per day.


4.13 Main Feeds
The survey found that 77% of the dairy farmers in the project area relied on pastures as the main feed
and 21% on napier grass and that only 2% considered hay as the main feed as shown in Figure 10
below. This confirms that this is predominantly a rainfed milk production system.
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                                                                                SDCP Baseline Survey Report




Figure 10: Main Animal feeds in the Project Area




Source: Survey, April 2009

This study found that there was little difference between DCA 1 and DCA 3 in terms of the main feed
sources as shown in Table 24 below.
Table 23: Main livestock feed in DCA 1 and DCA 3
                Main feed for livestock
           Pastures    Napier grass       Hay
 DCA 1       76.4%            18.4%       5.1%
 DCA 3          77%             23%        0%
 Total          77%              21%       2%
Source: Baseline Survey, April 2009
Further analysis of the distribution of the main feeds across the nine districts is shown in Figure 9
below. This analysis shows that napier grass forms the bulk of the livestock feed in Bungoma,
Nyamira and Nakuru Districts especially in areas where land holdings are very small and farmers have
adopted the zero grazing system. It is quite significant that it is only in Nakuru District where some
smallholder farmers rely on hay as the main livestock feed. Given that hay is purchased, it suggests
that such farmers don’t even have land on which to produce napier grass to meet the dairy needs
throughout the year.




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                                                                                      SDCP Baseline Survey Report



Figure 11: Main animal feeds by District




Source: Baseline Survey, April 2009

4.14 Supplementary Feeds
Supplementary feeds refer to anything that farmers fed the dairy cows over and above the main feed. This study
found that the most common feed supplements used by farmers were napier grass, maize stover commercial and
other feeds. Table 25 below shows that the average farmer in DCA 1 used 7.4 and 8.2 loads of napier grass and
maize stover; 1 kg of leucaenia, 1.5 kg of on-farm feed formulation and 9.4 kg of commercial feeds. This was
significantly higher supplement compared to what was happening in DCA 3 where maize stover and napier were
predominant but where the on-farm feed formulation and commercial feeds were significantly lower. This
analysis also revealed that there were only three farmers who had planted Calliadra and no farmer had mulberry
in the entire sample.

Table 24: Average quantity of supplementary feeds used during the wet season in DCA 1 and DCA 3
                                                                   On-Farm
 Dca     Napier Grass Maize                                        Formulation Commercial Other
 Area    (load)       Stover(load) Calliandra    Mulberry Lucaenia (kg)        Feeds (kg)  Feeds(kg)
 DCA1            7.46         8.24         N/A       N/A      1.00        1.54        9.46      1.99
 DCA3             3.44         13.91       N/A      -N/A      1.00         2.04         4.36         4.82
 Total            4.82           9.80      N/A      -N/A      1.00         1.86         6.73         3.50
Source: Baseline Survey, April 2009


4.15 Cost of Supplementary Feeds
This survey showed that farmers in DCA 1 incurred about Kshs 556 in providing supplementary feeds to their
dairy herd compared to the farmers in DCA 3 who incurred only Kshs 179 shilling in supplementary feeds as
shown in Table 25 below. This wide disparity in the cost of supplementary feeds reflects the higher level of

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  awareness and therefore willingness of farmers in DCA 1 on the role that feed planning in plays in increasing
  and stabilizing milk production as a result of the training that has been going on since the beginning of the
  programme.
  Table 25: Average cost of supplementary feeds in Kshs during the wet season in DCA 1 and DCA 3
    DCA         Napier    Maize                                                 On-Farm       Commercial      Other       Total
    Area        Grass     Stover       Calliandra     Mulberry     Lucaenia    Formulation      Feeds         Feeds        Cost
    DCA1         141.41   148.31             89.02        0.00         0.15           5.01        166.02        5.64      555.56
    DCA3         70.65       8.57            0.85         0.00         0.17         23.64           65.87        9.34     179.09
    Mean    100.64     67.81          38.23               0.00         0.16         15.74          108.32        7.77     338.67
  Source: Baseline Survey, April 2009


  This study found that 81% of the farmers in the project area also give supplementary feeds in addition to the
  main feed. Table 27 below shows the average daily amounts of feed supplements and the cost in each district
  across the project area during the rainy season.
  Table 26: Average cost of feed supplements during the wet season
                              Cost of                     Cost of     Cost of on-                    Cost of                          Total
                 Quantity     napier         Quantity     Maize       farm           Quantity of     commercia          Quantity of   Cost in
                 of napier    grass          of Maize     stover(     formulatio     on-farm         l feeds            commercia     Kshs
District         grass        (Kshs)         stover       Kshs)       n (Kshs)       formulation     (Kshs)             l feeds
Bomet            10.6923      27.74          11.4000      13.79       5.16           1.9286          9.42               1.4000        476.9
Kisii Central    2.1316       100.83         0            .00         .33            1.0000          .00                3.0000        215.3
Nyamira          1.0000       62.84          0            .00         .48            1.0000          39.10              1.0000        102.4
Nandi North       0           .00            1.0000       .12         .19            1.0000          290.42             30.0645       8,731.6
Trans Nzoia      2.7059       14.58          3.0000       .10         100.52         1.0000          34.88              1.1538        180.5
Bungoma          3.7411       86.41          0            .00         .00            0               121.13             7.2258        1,198.5
Lugari           6.5690       148.35         9.2000       7.80        11.43          4.8571          81.62              2.2273        1,283.6
Uasin Gishu      4.5119       423.09         10.1818      533.81      11.13          1.2000          571.55             5.2414        10,353.2
Nakuru           2.5000       23.21          1.0000       .93         1.87           1.0000          43.96              1.5024        126.9
Mean          4.8200       100.64            9.8000       67.81       15.74          1.8625          136.77             6.7255        2,098.8
  Source: Baseline Survey, April 2009



  This analysis suggests that farmers in Nyamira District incur the least expenses in supplementary feeds
  averaging Kshs 102 during the wet season while farmers in Uasin Gishu reported the highest cost of
  supplementary feeds averaging Kshs 10,353. Figure 12 below highlights the wide variation between the average
  cost of supplementary feeds across the districts.




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Figure 12: Average Daily Cost of Supplementary Feeds in Dry Season




Source: Baseline Survey, April 2009

These findings suggest that farmers in DCA 3 in Uasin Gishu incur the highest cost to produce milk by spending
an average of Kshs 430 per day during the dry season while their counterparts in DCA 3 in Kisii Central District
spent only Kshs 11 per day.


The feed situation deteriorates significantly during the dry season largely because the cost of supplementary
feeds increases across all the districts. Maize stover forms the bulk of supplementary feeds and is not available
during the dry season. Because most households have to choose between buying adequate animal feeds and
meeting the family food requirements during the dry season, livestock loose out.


An analysis of costs of supplementary feeds during the wet season is shown in Figure 11 below. Whereas the
costs appear lower in Kisii Central, Nandi North and Nakuru District, it is actually the diversion of the
household resources to meet the family’s upkeep during the dry season rather than costs of the dairy activities
that account for this lower cost. However, the cost of supplementary feeds in Lugari District increased by 86%
from Kshs 180 to Kshs 335 per day during the dry season.




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                                                                                          SDCP Baseline Survey Report



Figure 13: Average Costs of Milk Production (Wet Season)




Source: Baseline Survey, April 2009

4.16 Reasons why farmers don’t use supplements
The study also found that almost 48% of the farmers in DCA 1 did not give supplements to their dairy cows
because they could not afford to hire labour to manage fodder in their own farms. The most affected districts in
this respect were Kisii Central, Nakuru and Bomet Districts as shown in Table 27 below. The other most
common reasons why farmers did not use supplements in DCA 1 were that they either did not know the need to
give supplementary feeds or they did not feel it was necessary to do so. These responses suggest that there is
need to continue educating dairy farmers on the role of supplementary feeds as part of the extension message
even in DCA 1.




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                                                                                       SDCP Baseline Survey Report




        Table 27: Reasons why farmers don’t use supplements in DCA 1 by District
                                         Kisii   Nandi Trans                         Uasin
    Reason                         Bomet Central North Nzoia Bungoma Lugari          Gishu Nakuru Total
    Don't know how to grow
    fodder                         0%    0%      0%    0%      0%         0%         8%     0%         2%
    Don't have access to fodder
    seeds                          5%    0%      0%    8%      0%         33%        8%     0%         7%
    Can't afford the cost of feeds 5%    0%      25%   0%      0%         0%         8%     0%         7%
    Can't afford to hire labour to
    manage the fodder              81% 100% 50%        31%     0%         33%        8%     100%       48%
    Use own                        0%    0%      0%    46%     50%        0%         8%     0%         13%
    Give minerals only             0%    0%      13%   0%      0%         0%         0%     0%         2%
    No need                        10% 0%        13%   0%      50%        0%         42%    0%         15%
    Pasture is adequate            0%    0%      0%    15%     0%         0%         0%     0%         3%
    Lack of enough land            0%    0%      0%    0%      0%         33%        17%    0%         5%
    Total                          100% 100% 100% 100% 100%               100%       100%   100%       100%
        Source: Baseline Survey, April 2009

Analysis of farmers response in DCA 3 yielded slightly different reasons for not using supplementary feeds as
shown in Table 28 below. It showed that 38% of the dairy farmers responded that they could not afford to hire
labor to manage the fodder. This was particularly in Bomet, Nandi North and Nakuru Districts where 87%, 67%
and 60% of the farmers respectively did not use supplementary feeds because the cost of labour was prohibitive.
The second most important reason that farmers cited was lack of knowledge of how to grow fodder particularly
in Lugari, Uasin Gishu and Bungoma where 50%, 31% and 27% of the dairy farmers said they did not know.
These finding once again suggest that the SDCP should explore any technologies that reduce the cost of fodder
production while continuously improving the delivery of the effectiveness of the extension messages.




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Table 28: Reasons why farmers don’t use supplements in DCA 3 by District
                                    Kisii     Nandi     Trans                   Uasin
 Reasons                     Bomet Central North        Nzoia    Bungoma Lugari Gishu Nakuru Total
 Don't know how to grow
 fodder                          0%        0%        0%       8%      27% 50%       31%   0% 16%
 Don't have access to
 fodder seeds                    0%       50%        0%      15%       0% 20%       23%   0% 15%
 Can't afford the cost of
 feeds                           7%        0%       20%       8%       0%   0%       8%   0%    5%
 Can't afford to hire labour
 to manage the fodder           87%       10%       60%      54%       0% 10%       23%  67% 38%
 Use own                         7%       40%        0%      15%       0%   0%       0%  33% 10%
 No need                         0%        0%        0%       0%       0%   0%       8%   0%    1%
 Pasture is adequate             0%        0%        0%       0%      73% 10%        0%   0% 11%
 Has less cows                   0%        0%       20%       0%       0%   0%       0%   0%    1%
 New in the business             0%        0%        0%       0%       0% 10%        0%   0%    1%
 Lack of enough land             0%        0%        0%       0%       0%   0%       8%   0%    1%
  Total                        100%      100%     100%      100%     100% 100% 100% 100% 100%
Source: Baseline Survey, April 2009


4.17 Contingency measures to ensure milk production throughout the year
Smallholder dairy farmers employ various strategies to stabilize milk production and alleviate the limited feed
supply. These measures include feeding of crop by-products (such as maize stover), using green maize both as
food and feed (through thinning), fodder cultivation on roadsides, reliance on fodder markets and buying
concentrates. Silage making is not common. The practice of feeding crop by-products also serves to increase
efficiency between the livestock and crop enterprises through nutrient cycling, an important factor given the
deficiency of important soil nutrients resulting from the intensive cropping.


The most common contingency measure used by dairy farmers in DCA 1 to ensure stability in milk
production throughout the year was feed conservation which was reported by 63% of farmers. The
second most common contingency measure was buying outside the farm in which was undertaken by
22% of the farmers in DCA 1 as shown in Figure 14 below.




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Figure 14: Feed Contingency Measures in DCA 1




Source: Baseline Survey, April 2009

This study found that 66% of the farmers in DCA 3 used feed conservation as contingency to stabilize
milk production throughout the year as shown as Figure 15 below. This is not significantly different
from DCA 1 and suggests that changing farmers’ practices to adopt feed conservation technologies is a
long term goal.
Figure 15: Feed Contingency Measures in DCA 3




Source: Survey, April 2009


There were wide disparities between the districts in DCA 1 in the preferred contingencies that farmers
adopted to ensure stable milk production. For instance, this survey found that feed conservation was
most preferred contingency by farmers in Nandi North (91%), Lugari (89%) and Bomet (71%) as
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shown in Table 29 below. This suggests that SDCP extension messages on feed conservation have
been well received or are building on an existing body of knowledge from previous interventions in the
same areas. The second most common contingency measure by farmers was to purchase feeds from
other farmers. This study found that 22% of the farmers in DCA 1 preferred this option especially in
Bungoma (52%) and Nakuru (44%). Purchasing feeds from other farmers increases the viability of
smallholder dairy farming even where the land holding is very small. This is because it provides a
ready market for feed resources which enables such farmers to sustain their dairy herd and creates
income opportunities for crop farmers.
Table 29: Feed contingency measures in DCA 1
         Feed contingency measures taken to ensure milk production throughout the year
                          Kisii          Nandi Trans                Uasin
District           Bomet Central Nyamira North Nzoia Bungoma Lugari Gishu Nakuru Total
Feed conservation     71%     50%    61% 91% 83%          22% 89% 60%        36% 63%
Contracting other
farmers               24%       3%    0%    9%    0%       0%   0%     3%    21%    7%
Purchasing from
outside farm           4%     29%    39%    0%    6%      52%   5% 20%       44% 22%
Moving animals to
greener pastures       0%       3%    0%    0%    6%       0%   5%     5%     0%    2%
Continous planting     0%     15%     0%    0%    4%      22%   0%     5%     0%    5%
None                   0%       0%    0%    0%    0%       4%   0%     8%     0%    1%
Total                100% 100%      100% 100% 100%       100% 100% 100% 100% 100%
Source: Baseline Survey, April 2009

The survey found that feed conservation was also the most common contingency to sustain milk
production throughout the year in DCA 3 which was cited by 66% of the farmers as shown in Table 30
below. However, Nandi North (98%), Lugari (97%) Uasin Gishu (80%) Bomet (72%) and Trans
Nzoia(72%) registered the highest adoption rate of feed conservation. The second most important
contingency in DCA 3 that farmers reported was purchasing feeds from other farmers. This was most
common in Nyamira (54%) and Nakuru (31%) Districts which is makes smallholder dairy farming
viable because the market for feed resources enables farmers with small land holding sustain to keep
dairy cows. All other contingencies were only reported by about 14% of the dairy farmers in DCA 3.




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Table 30: Feed contingency measures in DCA 3
         Feed contingency measures taken to ensure milk production throughout the year
                          Kisii          Nandi Trans                Uasin
District           Bomet Central Nyamira North Nzoia Bungoma Lugari Gishu Nakuru Total
Feed conservation     72%     69%    44% 98% 72%          28% 97% 80%        31% 66%
Contracting other
farmers               13%       0%    2%    0%    5%       2%   0%     8%    37%    7%
Purchasing from
outside farm          10%     22%    54%    0% 21%        23%   0%     8%    31% 20%
Moving animals to
greener pastures       0%       2%    0%    0%    2%       7%   3%     0%     0%    2%
Continous planting     5%       0%    0%    2%    0%      14%   0%     4%     0%    2%
None                   0%       7%    0%    0%    0%      26%   0%     0%     0%    3%
Total                100% 100%      100% 100% 100%       100% 100% 100% 100% 100%
Source: Baseline Survey, April 2009

4.18 Cost of Milk Production
To calculate the cost of milk production in each district, we considered the semi-zero grazing production system
because this was the most common system. We however encounted two challenges in computing the cost per
litre. The first one was that farmers did not keep consistent records of their costs and therefore the costs used
were based on memory recall which introduces errors. The second challenge was that the farmers did not assign
a monetary value on their management input and family labour used in the dairy enterprise. This implied that
computing the cost of milk production without including the imputed costs significantly underestimated the cost
of milk production. To overcome these hurdles, we assumed that the least monthly cost of casual labour in the
programme area of Kshs 600 per month reflected the imputed labour input for each member of household in the
dairy enterprise. To arrive at the total cost of milk production, we then added the cost of all supplementary feeds
from farmers recall, the cost of water per month and the monthly cost of permanent and casual employees. We
then divided these costs with the monthly milk production during the dry and wet season to compute the cost per
litre. Using this approach, this study found that farmers in Trans Nzoia, Kisii Central and Nyamira Districts had
the highest cost of milk production of Kshs 34.50 per litre and Kshs 32.50 per litre during the dry season. This
high cost was attributed to the fact that there were large households in these districts, low levels of milk
production.


On the other hand, farmers in Nandi North, Bomet and Uasin Gishu Districts had the least cost of milk
production during the dry which on average was Kshs 19.60, Kshs 24.10 and Kshs 25.6 per litre respectively.
This low cost of production could be attributed to availability of low cost pastures, using of rivers and other low
cost water sources and substituting hired labour with the low cost family labour.
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Figure 16 below shows the analysis of the cost of milk production per litre during the dry season.
Figure 16: Cost of Milk Production during the Dry Season




Source: Baseline Survey, April 2009

This survey showed that the cost of milk production in the wet season was much lower than in the dry season as
shown in Figure 17 below. In some cases, this cost was reduced by half during the wet season. This finding
confirms that smallholder dairy farming system is rainfed.
Figure 17: Cost of Milk Production during the wet season




Source: Baseline Survey, April 2009

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4.19 Water Sources
To estimate the risk of contacting water borne diseases, incentives and the cost of dairy farming, to
participate in community projects, respondents were asked to indicate the sources from which they
drew water for domestic and livestock use. Figure 18 below shows that 33% of the households in the
project area get water from boreholes and only 12% has access to piped water. This finding shows the
reason why dairy enterprise creates employment opportunities because keeping a dairy cow fully
supplied with water is a labor intensive activity in which many households resort to hired labor or
engage family labor on a full time basis.
Figure 18: Main Sources of Water during the Wet Season




Source: Baseline Survey, April 2009

Further analysis of this data showed that 33.3% of the households in DCA 1 relied on boreholes which
were very close to the 32.3% of households in DCA 3. However, 21% of the farmers DCA 1 relied on
river water compared to 29.3% of the households in DCA 3. Table 31 below shows the proportion of
households in DCA 1 and DCA 3 based on their main sources of water during the wet season.




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Table 31: Main water sources in DCA 1 and DCA 3 during wet season
 The main source of water during the wet
 season
                      DCA 1 DCA 3        Total
 River                  21%       29%     26%
 Piped water            13%       11%     12%
 Protected spring         2%       1%      2%
 Unprotected spring       2%       1%      2%
 Open well                7%       5%      6%
 Protected well         10%       10%     10%
 Roof catchment           6%       6%      6%
 Dam/Lake                 2%       1%      1%
 Earth pan                1%       1%      1%
 Borehole               33%       32%     33%
 Shallow well             1%       3%      2%
 Total                 100%      100% 100%
Source: Baseline Survey, April 2009

This study suggests that many of the water sources are seasonal because the proportion of farmers who
rely on other sources during the dry season increases significantly as shown in Table 32 below.
However, farmers rely on multiple water sources at any time but this analysis concentrated on the main
water source.
Table 32: Main water sources in DCA 1 and DCA 3 during dry season
 The main source of water during the dry season
                      DCA1      DCA3 Total
 River                   35%      41%         39%
 Piped water             15%       9%         11%
 Protected spring          3%      1%          2%
 Unprotected spring        1%      1%          1%
 Open well                 6%      4%          4%
 Protected well            3%     14%         10%
 Roof catchment            1%      0%          0%
 Dam/Lake                  2%      1%          1%
 Earth pan                 0%      0%          0%
 Borehole                33%      25%         28%
 Buying water              0%      2%          1%
 Shallow well              1%      3%          2%
 Total                  100% 100%            100%
Source: Baseline Survey, April 2009

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Further analysis of the water sources shown in Table 33 below indicates that Bomet District has the
widest diversity of the water sources during the wet season. This is because the district within the Mau
Forest Complex whereas households in Nakuru District have only four alternative sources even within
the wet season.
Table 33: Main source of water during the wet season by District
                               Kisii                Nandi     Trans                       Uasin
                      Bomet    Central    Nyamira   North     Nzoia    Bungoma   Lugari   Gishu   Nakuru
 River                  36%       68%        35%     35%        14%        15%      8%     18%      19%
 Piped water             3%         5%        5%     14%        20%         0%      2%       2%     52%
 Protected spring        2%         0%        0%      0%        10%         0%      1%       0%      0%
 Unprotected spring      8%         0%        0%      0%         2%         0%      2%       0%      0%
 Open well               9%         2%        1%      0%        20%        14%      4%       2%      0%
 Protected well          2%         7%       43%      2%        10%        17%      1%       7%      0%
 Roof catchment          7%         0%       14%      0%         1%         0%      9%       2%     15%
 Dam/Lake                8%         2%        0%      0%         1%         0%      1%       0%      0%
 Earth pan               6%         0%        0%      0%         1%         0%      0%       0%      0%
 Borehole               14%       17%         3%     49%        20%        54%     71%     69%       1%
 Shallow well            3%         0%        0%      0%         1%         0%      0%       0%     13%
Source: Baseline Survey, April 2009

4.20 Adequacy of Water
On the question of the adequacy of the water throughout the year, Table 34 below shows that that 89%
of the households have adequate water throughout the year while 11% do not. Further analysis of this
variable showed that 10.6% of the farmers in DCA 1 did not have adequate water throughout the year
compared to 11% in DCA 3.

Table 34: Status of water adequacy throughout the year in DCA 1 and DCA 3
              Is the water adequate
               throughout the year?

 DCAs           Yes           No         Total
 DCA 1           89.3%        10.7%      100%
 DCA 3            88.9%       11.1%      100%
 Total             89%           11%     100%
Source: Baseline Survey, April 2009


However, there is wide disparity in adequacy of water between the district in each DCA. For instance,
44% of the households in Nakuru District without adequate water throughout the year while
households in Nyamira and Bungoma have adequate water throughout the year as shown in Table 32
below.



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Table 35: Water adequacy throughout the year
                   Is the water adequate
                   throughout the year?
  District         Yes           No
 Bomet             85%           15%
 Kisii Central     83%           17%
 Nyamira             100%           0%
 Nandi North         99%            1%
 Trans Nzoia         93%            7%
 Bungoma             100%           0%
 Lugari              97%            3%
 Uasin Gishu         90%            10%
 Nakuru              56%            44%
 Mean                89%            11%
Source: Baseline Survey, April 2009


4.21 Choice of Animal Breeds
This survey found that 83% of the farmers in DCA 1 used milk yield as the most important
consideration in choosing the preferred dairy breed. There was wide disparity between districts on this
account. For instance, the largest proportion of farmers using milk yield are from Nakuru District
(97%) and Nyamira (95%) while Bomet (64%) had the least proportion as shown in Table 36 below.
The second consideration was disease resistance which accounted for 10% of the farmers in DCA 1.

Table 36 : Choice of Breeds by Districts in DCA 1
           The most important consideration in the choice of the breed in DCA 1
                   Milk     Growth Disease         Market Body        Feeding
 District          yield    rate     resistance value        weight behavior    Total
 Bomet                64%        2%        33%         0%        0%          0% 100%
 Kisii Central        68%        6%          3%       12%        6%          6% 100%
 Nyamira              95%        3%          0%        0%        0%          3% 100%
 Nandi North          88%        0%        13%         0%        0%          0% 100%
 Trans Nzoia          83%        6%        11%         0%        0%          0% 100%
 Bungoma              85%        0%        11%         0%        0%          4% 100%
 Lugari               84%        0%        11%         5%        0%          0% 100%
 Uasin Gishu          83%      15%           3%        0%        0%          0% 100%
 Nakuru               97%        0%          3%        0%        0%          0% 100%
 Total                83%        4%        10%         2%        1%          1% 100%
Source: Baseline Survey, April 2009

The same trend of using milk yield and disease resistance as the key considerations in the choice of
dairy breeds was also observed in DCA 3. However, Bomet district had a much higher proportion of
farmers that were using milk in the choice of the breeds in DCA 3 than in DCA 1. Similarly, 100% of

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the farmers in Nyamira District used milk yield in the choice of breeds in DCA 3 as shown in Table 37
below. These results suggest that other considerations played a minor role in the choice of dairy breeds
other than milk yield and disease resistance.

Table 37: Choice of Breeds by Districts in DCA 3
           The most important consideration in the choice of the breed in DCA 3
                   Milk Growth Disease            Market Body        Feeding
 District          yield rate       resistance value       weight behavior Total
 Bomet               93%       3%           5%        0%        0%         0%   100%
 Kisii Central       97%       0%           2%        2%        0%         0%   100%
 Nyamira           100%        0%           0%        0%        0%         0%   100%
 Nandi North         56%       0%         44%         0%        0%         0%   100%
 Trans Nzoia         70%       5%         26%         0%        0%         0%   100%
 Bungoma             86%       0%           7%        0%        0%         7%   100%
 Lugari              46%       1%         48%         0%        0%         4%   100%
 Uasin Gishu         74%       2%         16%         4%        0%         4%   100%
 Nakuru              96%       0%           2%        0%        0%         2%   100%
 Total               80%       1%         17%         1%        0%         2%   100%
Source: Baseline Survey, April 2009

4.22 Preferred Breeding Methods
The study found that 43% of the farmers in the programme area preferred to use AI services for
breeding while 57% preferred bull service. When the responses were disaggregated by DCAs, it
showed that 41% of farmers in DCA 1 preferred AI service compared to 46% with the same preference
in DCA 3. While we would expect that DCA 1 would have a higher preference for AI service given the
training that SDCP has carried out in the last two years, the survey suggests that other constraints in
service delivery may inform the farmers’ preference for bull service despite this knowledge. Table 38
below shows the results of this analysis.

Table 38: Status of preferred breeding method in DCA 1 and DCA 3
             Which breeding method do you
                     mostly prefer
                       Bull
 DCAs        AI      service      Local bull       Total
 DCA 1       41%           9%            50%       100%
 DCA 3       46%          10%            44%       100%
 Total       43%          10%            47%       100%
Source: Baseline Survey, April 2009
This study found a wide disparity in preference for bull service in DCA 1 and DCA 3. For instance,
98% of farmers in Bomet District preferred bull service in both DCA 1 and DCA 3 on one extreme


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while only 3% preferred bull service in Nakuru District in DCA 1 and 7% in DCA 3 at the other
extreme as shown in Figure 17 below. This disparity shows other factors may be at play.

Figure 19: Preference for Bull Service by District in DCA1 and DCA 3




Source: Baseline Survey, April 2009

4.23 Choice of the Preferred Breeding Methods

When the reasons for choosing the preferred breeding service were analyzed, nearly 56% of the
farmers selected the breeding method on the basis of cost of delivery of the service and only 25% on
the characteristics of the breed. When this data was disaggregated by DCAs, as shown in Table 39 it
showed that 59% of the farmers in DCA 1 selected the breeding methods on the basis of cost of
delivery compared to 53% in DCA 3 who used the same criteria.
Table 39: Reasons for bull preference between DCA 1 and DCA 3
 Reasons for bull preference               DCA 1 DCA 3 Total
 High production and better breeds             21%      29%   25%
 No AI services available                       0%       1%    0%
 Easily available and cheap                    59%      53%   56%
 Effective                                      2%       5%    4%
 Disease resistant                             16%      12%   14%
 Group owns the bull                            2%       0%    1%
 Local bull can't service exotic breeds         0%       0%    0%
 Total                                       100%     100% 100%
Source: Baseline Survey, April 2009


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These findings suggests the SDCP and other players need to educate farmers on the long term benefits
of making the right breeding choices while ensuring that competent AI service providers are
consistently available at affordable prices. This conclusion emerges from the fact that almost 6% of the
respondents could not access breeding services throughout the year.

4.24 Breeding Related Costs
Availability of artificial insemination services is key to the development of the dairy sector because it
provides several benefits to farmers. First, heifers born through AI service have a high market value
and secondly, farmers are able to get good quality heifers from genetically superior bulls cheaply and
conveniently. Thirdly, AI prevents losses from reproductive diseases such as Brucellosis and finally,
the use of AI services saves farmers the high costs of maintaining breeding bulls. Semen that is used
by AI service providers in Kenya is either sourced locally from Central Artificial Insemination Station
(CAIS) or is imported.


Table 40 below shows that farmers in DCA 1 paid an average of Kshs 770 for AI services using local
semen. Farmers in Bungoma District incurred the highest cost to access AI services paying an average
of Ksh 1,222 while farmers in Nandi North paid the least at Kshs 609. The cost in all other districts
was within these two extremes.

Table 40: Cost of AI service using local semen by districts in DCA 1
               The cost of using AI local semen in DCA 1
                                                       Std.
 District          Minimum Maximum Mean                Deviation
 Bomet             600          1,500        1,050     636
 Kisii Central     600          2,500        878       467
 Nyamira           600          700          643       53
 Nandi North       600          700          609       30
 Trans Nzoia       600          700          680       45
 Bungoma           600          3,000        1,222     710
 Lugari            750          1,000        943       97
 Uasin Gishu       600          1,200        910       225
 Nakuru            600          1,000        681       74
 Total             600          3,000        770       317
Source: Baseline Survey, April 2009



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Within DCA 3, the survey found that farmers in Lugari, Trans Nzoia, Bomet and Bungoma districts
incurred the highest cost for AI services using local semen while farmers in Uasin Gishu incurred the
least cost for the same service as shown in Table 41 below.
Table 41: Cost of AI service using local semen by districts in DCA 3
               The cost of using AI local semen in DCA 3
                                                       Std.
 District          Minimum Maximum Mean                Deviation
 Bomet             800          1,500        1,013      217
 Kisii Central     600          3,000        946        726
 Nyamira           600          800          700        69
 Nandi North       600          800          695        38
 Trans Nzoia       700          1,500        1,068      284
 Bungoma           600          2,000        907        341
 Lugari            600          3,000        1,187      594
 Uasin Gishu       600          1,700        694        168
 Nakuru            600          3,000        845        398
 Total             800          1,500        1,013      217
Source: Baseline Survey, April 2009

As expected, the survey found that cost of AI service using imported semen in DCA 1 in all the
districts was much higher than the cost of using local semen. For instance, the study showed that
farmers in Lugari District on average paid Kshs 2,750 for AI service using imported semen. The
survey further showed that on average, farmers in Kisii Central incurred the least expense of Kshs 885
to access AI service using imported semen while the cost in other districts in DCA 1 varied between
these two extremes as outlined in Table 42 below.




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Table 42: Cost of AI service using imported semen by districts in DCA 1
        The cost of AI using imported semen in DCA 3 in Kshs
                                                     Std.
 District         Minimum Maximum Mean               Deviation
 Bomet             800          6,000      2,500      2,386
 Kisii Central     500          2,000      885        321
 Nyamira           1,200        1,500      1,300      141
 Nandi North       700          1,200      1,033      289
 Trans Nzoia       1,500        3,000      2,500      866
 Bungoma           1,300        3,000      2,483      806
 Lugari            1,500        4,000      2,750      1,768
 Uasin Gishu       500          6,000      1,383      854
 Nakuru            800          6,000      2,500      2,386
 Total             500          2,000      885        321
Source: Baseline Survey, April 2009


The cost of AI service using imported semen was higher across all the districts in DCA 3 as shown in
Table 43 below. These results were significant because on one hand, they also showed that farmers in
Kisii Central in DCA 3 were incurred the highest average cost of AI service using imported semen of
Kshs 2,400 per service while on the other, farmers in the neighboring Nyamira District incurred only
Kshs 1,064 for the same service. The cost in all the other districts fell within these two extremes.
Table 43: Cost of AI service using imported semen by districts in DCA 3
          The cost of AI using imported semen in DCA 3 in Kshs
 District          Minimum Maximum Mean               Std. Deviation
 Bomet              1,500        3,000     2,000      632
 Kisii Central      1,200        3,000     2,400      1,039
 Nyamira            600          2,000     1,064      371
 Nandi North        1,200        2,500     1,426      326
 Trans Nzoia        1,200        1,500     1,243      113
 Bungoma            600          1,700     1,238      388
 Lugari             1,000        10,000    2,488      3,052
 Uasin Gishu        1,500        2,800     2,150      919
 Nakuru             600          10,000    1,518      1,039
 Total              1,500        3,000     2,000      632
Source: Baseline Survey, April 2009


These results suggest that there are market factors in DCA 1 that are working to the advantage of
farmers to access the AI services at more competitive prices than their counterparts in DCA 3. One of

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these factors is the competition among AI service providers in DCA 1 which has forced them to reduce
the cost of delivery of AI services.

Bull service is still preferred by some farmers particularly where the AI costs are considered prohibitive or in
areas where the road infrastructure is poor and the services unreliable. Farmers in DCA 1 paid on average Kshs
113 to access bull service compared to their counterparts in DCA 3 who paid Kshs 172 as shown in Table 44
below.
Table 44: Cost of bull service in DCA 1 and DCA 3
 DCAs        Minimum      Maximum       Mean           Std. Deviation
 DCA 1      Free               600       113.30              170.367
 DCA 3      Free               700           172.43           171.418
 Total    Free                  700          145.36           173.296
Source: Baseline Survey, April 2009


While the average cost of accessing bull service in the programme area was Kshs 145, there were
many farmers who allowed the use of their bulls for free especially to their neighbours or relatives.
Table 39 below is an analysis of the costs of accessing bull service disaggregated by districts in the
project area. It shows that there were no farmers in the sample from Nandi North District who had paid
for using bull service while farmers in Nyamira paid an average of Kshs 340 per service. The cost of
bull service disaggregated by districts is shown in Table 45 below.

Table 45: Cost of bull service by District in Kshs
 District          Minimum    Maximum            Mean         Std. Deviation
 Bomet             Free            200             12.50             44.859
 Kisii Central     Free               300         200.00              64.327
 Nyamira           Free               600         340.52             147.034
 Nandi North       Free               Free              .00             .000
 Trans Nzoia       Free               700         152.14             212.888
 Bungoma           Free               500         290.42             157.817
 Lugari            Free               400         225.40              94.984
 Uasin Gishu       Free               500             85.48          156.652
 Nakuru            200                400         272.73              64.667
 Total           Free                 700         145.36             173.296
Source: Baseline Survey, April 2009

4.25 Breeding Efficiency
One of the reasons why farmers insist on keeping bulls is because of the high number of repeat inseminations
from AI services before conception is achieved which increases the breeding costs. This study found that the


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maximum number of inseminations before conception in DCA 1 was 15 in Kisii Central but a minimum of 2 in
most of the other districts as shown in Table 46 below.

Table 46: Maximum Number of inseminations before conception in DCA 1
 District        Maximum
 Bomet                  3
 Kisii Central           15
 Nyamira                  3
 Nandi North              2
 Trans Nzoia              5
 Bungoma                  3
 Lugari                   2
 Uasin Gishu              2
 Nakuru                   4

However in DCA 3, the maximum number of inseminations per conception was 8 in Uasin Gishu and Nakuru
Districts as shown in Table 47 below. This analysis suggests that the SDCP should increase awareness of
farmers on how to evaluate the delivery of AI services to increase breeding efficiency.
Table 47: Maximum number of inseminations before conception in DCA 3
 District        Maximum
 Bomet                  2
 Kisii Central            4
 Nyamira                  2
 Nandi North              2
 Trans Nzoia              4
 Bungoma                  7
 Lugari                   5
 Uasin Gishu              8
 Nakuru                   8
Source: Baseline Survey, April 2009


4.26 Calving Interval
One of the issues that were tracked in this survey was the calving interval. This is the amount of time
(days or months) between the birth of a calf and the birth of a subsequent calf, both from the same
cow. Table 48 below shows that the calving interval in DCA 1 was 450 days while the calving interval
in DCA 3 was 480 days. However, the calving interval is not statistically different between the DCA 1
and DCA 3. This could probably be because it takes a long time to reduce the calving interval.




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Table 48: The calving interval in the dairy herd (in days) in DCA 1 and DCA 3
DCA Area District      Minimum Maximum Mean              Std. Dev
  DCA1    Bomet                  270           720     360         120
          Kisii Central          270           720     450         160
          Nyamira                270           720     360         100
          Nandi North            360           720     300         60
          Trans Nzoia            360           720     300         60
          Bungoma                270           720     360         150
          Lugari                 360           720     16.3        150
          Uasin Gishu            270           720     360         100
          Nakuru                 360           720     390         60
          Total                  270           720     450         140
DCA3      Bomet                  270           720     360         90
          Kisii Central          360        1000       540         150
          Nyamira                270           450     360         60
          Nandi North            360           720     400         120
          Trans Nzoia            270        1000       450         150
          Bungoma                270           360     300         90
          Lugari                 330           720     400         90
          Uasin Gishu            360           720     400         90
          Nakuru                 360           720     450         150
          Total                  270        1000       480         150
Source: Baseline Survey, April 2009

4.27 Milk Production, Sales and Consumption
This study found that the average farmer in DCA 1 produced 8.84 litres of milk per day compared to
farmers in DCA 3 who produced 9.81 litres per day. The study also showed that farmers in DCA 1 and
DCA 3 sold about the same amount of milk which was about 6.04 litres per day. This survey therefore
suggests that the extra milk produced above this threshold in DCA 3 is currently retained for home
consumption as shown in Table 49 below.
Table 49: Average milk production, sales and home consumption in DCA 1 and DCA 3
                   Milk production in   Milk sold in   Home consumption
 DCAs              litres/day           litres/day     in litres/day
 DCA 1    Mean     8.84                 6.03           2.93
 DCA 3    Mean     9.81                 6.38           3.26
 Total    Mean     9.39                 6.23           3.11
Source: Baseline Survey, April 2009
This study found that the average household produces 9.4 litres of milk per day of which 3.1 litres are
retained for home consumption while 6.2 litres are sold. The average price realized was computed by
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taking the mean price from the main outlets in each district. Table 50 below shows the average milk
prices in various market outlets in DCA 1 and DCA 3.
Table 50: Average milk price in various outlets in DCA 1 and DCA 3
             Buying price      Buying price by   Buying price                      Buying       Buying price     Mean
              by Informal           dairy             by         Buying price     price by        in other      Price in
             milk traders in   cooperatives in   neighbours      by milk bar in processors       outlets in    Kshs/litre
    DCAs       Kshs/litre        Kshs/litre      in Kshs/litre     Kshs/litre   in Kshs/litre    Kshs/litre
    DCA 1             25.33              24.09          27.43            28.37        21.93            27.44       25.77
    DCA 3             21.81             22.28           26.61            22.30          21.40          25.87       23.38
    Total          23.13            23.29               26.98            27.10          21.76          26.48       24.79
Source: Baseline Survey, April 2009


The average daily revenue from milk sales was computed by multiplying the total production with the
average price. Total production which included home consumption was used because it included this
invisible income which could not be computed from milk sales. This analysis shows wide variation
between the areas in the project as shown by Table 51 below. The analysis shows that smallholder
dairy farmers in Lugari Districts received the highest milk income while farmers in Bomet District had
the lowest milk income averaging Kshs 86/day. This arises from the fact the price of milk is lower in
Bomet than all the other districts in the project area.
Table 51: Average Milk Production, Sales and Consumption by District
                    Average milk Average Milk Household                          Average        Average
                    production in Sales in         Milk consumption in           Price          Revenue
    District        litres/day      litres/day     litres/day                    Kshs/litre     Kshs/day
    Bomet                       6.8            4.3                 2.5           19.8           86
    Kisii Central              10.5            5.7                 4.6           24.4           140
    Nyamira                     7.5            3.9                 3.6           26.3           102
    Nandi North                10.2            6.2                 3.5           22.1           137
    Trans Nzoia                 7.8            5.6                 2.8           22.8           128
    Bungoma                     7.4            5.1                 2.3           26.5           134
    Lugari                     11.8            8.8                 2.9           26.8           236
    Uasin Gishu                11.8            7.8                 4.0           26.2           206
    Nakuru                     11.1            8.4                 2.4           23.1           195
    Mean                        9.4            6.2                 3.1           24.8           154
Source: Baseline Survey, April 2009

When the milk revenue was disaggregated by DCAs as shown in Figure 20 below, it showed that
farmers in DCA 1 got between Kshs 98 and Kshs 349 in Bomet and Lugari District respectively. On
the other hand, farmers in DCA 3 were getting between Kshs 135 and Kshs 337 per day in Trans Nzoia
and Uasin Gishu Districts.
.
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Figure 20: Average Dairy Revenue from Milk Sales in Kshs




Source: Computed by analyzing milk sales and average price realized from different outlets
4.27.1 Milk Bars and other milk outlets
Key informant interviews with milk bar operators during the survey showed that they get their milk
from farmers who produce about 8 liters per day. However, very few operators conducted quality tests
before accepting the deliveries because they had been in business with same farmers for a long time
they had built confidence between them. Most milk bar operators paid for the milk delivered the same
day. The milk bars/retail shops buy the milk at an average price of ksh.25.00 then sell at either
Ksh.35.00 or 40.00 depending on supply and demand.


Bicycles were the principal mode of transport for milk deliveries to milk bars either by the operators or
the farmers. Some milk bars were also processing the milk into mala and yoghurt are the main milk
products processed and they sell at ksh.35.00 and ksh.45.00 a litre respectively in Ndalu sub-location
while those of Bukembe did not have similar training.


The performance of the milk sales was dependent on the season of the year and most milk bar
operators confirmed that low milk sales are the major constraints hindering business growth. In dry
seasons the milk supply is low; the milk bars therefore refer their customers to other milk bars or
reduce the number of customers.
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4.28 Milk handling practices
The perishable nature of milk imposes the need for adequate and clean water for cleaning equipment
such as milk cans, while the long distance (often on rough roads) to the collection centres, cooling
plants and processing factories creates the need for well-maintained feeder roads. To determine
compliance to good milk handling practices, the study team observed the type of milk handling
practices that were being used. The survey found that 90% of the farmers in the project area had
moderate milk handling practices (they carried out hand and udder washing and used aluminum
equipment) but that only 3% of the farmers met all the recommended milking practices as shown in
Figure 21 below. Of particular concern is that team observed that 7% of the farmers had poor milk
handling practices which can compromise the market for those that have adopted recommended
practices.
Figure 21: Milk handling practices




Source: Baseline Survey, April 2009

Table 52 below illustrates the results of further analysis to identify the districts where particular type of
behavior was prevalent. In general, the problems of milk handling affected the entire program area
however certain areas such Mubere Sub-location, Kaibei Location, Endebes Division of Trans Nzoia
District were most affected in which 45% of the farmers had poor milk handling practices.

Table 52: Milk handling practices by District
 District         Poor Moderate Good  Total
 Bomet              4%     95%     1%   100%

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 Kisii Central       2%           97%    2%     100%
 Nyamira             2%           94%    4%     100%
 Nandi North         3%           97%    0%     100%
 Trans Nzoia        30%           67%    3%     100%
 Bungoma             9%           90%    1%     100%
 Lugari              5%           89%    6%     100%
 Uasin Gishu         6%           83%   11%     100%
 Nakuru              2%           98%    0%     100%
 Total               7%           89%    3%     100%
Source: Baseline Survey, April 2009

This survey showed that Trans Nzoia District faced the greatest challenge in milk handing because
about 30% of the farmers were observed to have poor milk handling practices.


Informal milk traders only checked for cleanliness without conducting any quality tests on the milk,
however the milk bar operators who purchased milk from informal traders conducted quality tests.


Raw milk is a highly perishable and easily contaminated product. Processing technologies aim at
producing high quality fresh dairy products and increase the shelf life long enough to go through the
distribution system. The quality of the final product depends on milk hygiene and quality of the raw
material. The following methods were the quality of milk and dairy products.


Quality Testing Technologies
The type of dairy cow and its diet can lead to differences in colour, flavour, and composition of milk.
Infections in the animal that also cause disease may also be passed on to the consumer through milk. It
is therefore important that quality control tests are carried out to ensure that the bacterial activity in
raw milk is of an acceptable level, and that no harmful bacteria remain in the processed product . The
following technologies were largely being used in testing the quality of milk by dairy MSEs in the
study areas.
Organoleptic test - This is the cheapest method and one of the most reliable ones when used by an
experienced person. It entails developing a sense of smell and sight for high quality milk. A skilled
worker can detect adulteration or spoilage by sight and smell and use other tests simply to confirm.
Lactometer – which is quite effective in determining possible adulteration of milk especially with
water and in some cases with milk powder. The price of the lactometer ranged between Kshs 300 –
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Kshs 650 in the market. The study found out that KDB was not aware that dairy MSEs are using this
technology but it is routinely used by most milk bars by some hawkers in Nakuru.


Alcohol Clot Tests -This is particularly in use by the trained yoghurt processing dairy micro-
enterprises especially in Nakuru. Unlike cooperatives which used the more expensive and automated
alcohol guns, dairy MSEs used a simple but effective system. This test provides an indication of the
bacterial load of the milk and the potential for spoilage. This technology was again found to be widely
in use by milk bars who have been trained in one hawker in Nakuru.


Clot Boiling Method: In the absence of these other tests, most dairy MSEs simply boiled small
samples of milk (with a candle on a spoon) and observed whether they cuddle.


Match Stick Test: This is one of the methods devised by dairy MSEs to test milk for water
adulteration. The head of a matchstick is dipped in milk and struck. If the milk is wholesome, it lights,
if not, it doesn’t.


The Polythene Test: Milk is poured into a nylon paper and allowed to flow. If it flows without leaving
stains on the paper then it may suggest presence of water. If it stains the paper then it’s considered
wholesome. These were the most commonly used methods employed in the milk bars visited.


4.29 Milk Marketing Constraints
Analysis of the milk marketing constraints facing farmers in DCA 1 showed that almost 43% faced
problems of low prices and 41% had problems lack of cooling facilities as shown in Table 53 below.
The other constraints were relatively minor accounting for only 16%. For instance, in Nandi North
District, 96% of farmers in the survey lacked chilling facilities.
Table 53: Milk Marketing Constraints in DCA 1
            Which constraints do you face in marketing your milk in this area in DCA 1
                              Kisii               Nandi Trans                        Uasin
 District             Bomet Central Nyamira North Nzoia Bungoma Lugari Gishu Nakuru Total
Harassment by local   3%      12%      0%         0%      0%      0%          0% 0%        5% 3%
authorities
Harassment by KDB     0%      6%       0%         0%      0%      0%          0% 3%        3% 2%
inspectors
Low milk prices         97%     33%     11%     0%    25%    68%      53% 47%      51%      43%
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Delayed payments         0%      0%    8%    4%     0%    8%      27%   3%    0%       4%
Defaulters               0%      0%    0%    0%     0%    8%      0%    0%    0%       1%
No problems              0%      3%    0%    0%     0%    0%      0%    0%    0%       0%
Lack of refrigeration    0%      15%   76%   96%    75%   16%     0%    44%   41%      41%
Lack of market           0%      0%    0%    0%     0%    0%      7%    0%    0%       0%
No standard              0%      30%   3%    0%     0%    0%      7%    3%    0%       5%
measurements for milk
Bad roads                0%      0%    0%    0%     0%    0%      7%    0%    0%       0%
Total                    0%      0%    3%    0%     0%    0%      0%    0%    0%       0%
Source: Baseline Survey, April 2009


In DCA 3, the farmers who reported that they had no marketing constraints accounted for only 27% of
the farmers which 63% were in Nyamira District. In general, the most pressing marketing constraint in
DCA 3 was low milk prices which affected 43% of all the farmers as shown in Table 54 below.




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Table 54: Milk Marketing Constraints in DCA 3
                              Which constraints do you face in marketing your milk in this area ?
                              Kisii                Nandi Trans                             Uasin
 District – DCA 3       Bomet Central Nyamira North Nzoia Bungoma Lugari                   Gishu    Nakuru    Total
 Harassment by local    3%       50%      0%         0%        0%     0%         2%        0%       0%        6%
 authorities
 Harassment by KDB      5%       0%       0%         0%        0%     0%         7%        11%      2%        3%
 inspectors
 Low milk prices        90%      18%      6%         40%       54%    48%        50%       55%      39%       43%
 Delayed payments       0%       5%       30%        20%       3%     33%        29%       19%      4%        17%
 Defaulters             0%       0%       0%         0%        5%     0%         5%        4%       0%        2%
 Corrupt                0%       0%       0%         0%        0%     0%         0%        2%       0%        0%
 management
 committees
 No problems            3%       20%      63%        35%       38%    10%        3%        9%       54%       27%
 Returned milk          0%       0%       0%         5%        0%     0%         2%        0%       0%        1%
 Lack of market         0%       0%       0%         0%        0%     2%         0%        0%       0%        0%
 Low sales              0%       0%       0%         0%        0%     5%         0%        0%       0%        1%
 No standard            0%       0%       0%         0%        0%     0%         2%        0%       0%        0%
 measurements for
 milk
 Unable to satisfy      0%       8%       0%         0%        0%     0%         0%        0%       0%        1%
 market demands
 Bad roads              0%       0%       0%         0%        0%     2%         0%        0%       0%        0%
 Total                  100%     100%     100%       100%      100%   100%       100%      100%     100%      100%
Source: Baseline Survey, April 2009
These results suggest that SDCP should intensify its efforts to help farmers in the project area to get
better organized so that they can have more bargaining power in contracts that they negotiate with all
manner of milk buyers.

4.30 Milk Processing
This study showed that only 7.3% of the farmers in the project area engage in on-farm milk processing
activities. The key products that they produce are 2.9% Musik (a traditional fermented milk flavored
with herbs), Mala 2.8% and yoghurt 0.5% as shown in Figure 23 below. This analysis suggests that
these products are targeted at tiny niche markets and not for the mass market.




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Figure 22: On-farm Milk Processing




Source: Baseline Survey, April 2009

Further analysis of the on-farm dairy processing activities showed that Mursik and Mala were the
products that were produced in most of the districts at a volume of between 3 and 6 litres while ghee
was only in one farm in Trans Nzoia District as shown in Table 55 below.
Table 55: Mean production of on-farm dairy products
                 Volume of    Volume of    Volume of   Volume of
 District         mursik       yoghurt       ghee        mala
 Bomet                1.88
 Kisii Central          .50        6.00                      10.75
 Nyamira                                         .50          3.57
 Trans Nzoia           7.50       10.00                       7.50
 Bungoma                                                      3.62
 Lugari                0.46
 Uasin Gishu           5.75                                   .250
 Nakuru                            6.00
 Total                 3.18         7.00         .50          5.81
Source: Baseline Survey, April 2009



4.31 Skills Required to Improve Profits in Dairy Farming

This study found that 77% of the farmers in the project area felt that they needed to acquire animal
husbandry related skills to improve profitability of the dairy enterprise as shown in Figure 24 below.
The other skills in high demand were breeding, management and record keeping which farmers
considered to be limiting their capacity to achieve higher profits from the dairy enterprise.


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Figure 23: Skills Needed to Increase Profitability of Dairy Enterprise




Further analysis of these skills requirements showed that in all the districts, 76% of the farmers required animal
husbandry related skills in DCA 1 as shown in Table 56 below.
Table 56: Farmers who need skills to increase profitability of dairy enterprise in DCA 1
                               Kisii                 Nandi Trans                           Uasin
 District           Bomet      Central Nyamira North Nzoia              Bungoma Lugari Gishu              Nakuru Total
 Disease control         4%          6%        3%        0%       11%         22%     11%       5%          10%    8%
 Record keeping         42%          0%       18%        0%        0%          0%       0%      3%           0%    8%
 Milk handling           0%          6%        8%        0%        2%         22%       5%    13%            3%    6%
 Husbandry              53%         88%       71% 100%            80%         52%     84%     73%           87% 76%
 None                    0%          0%        0%        0%        7%          0%       0%      8%           0%    2%
 Biogas                  0%          0%        0%        0%        0%          4%       0%      0%           0%    0%
 Total                 100%       100%       100% 100%          100%        100% 100%        100%          100% 100%
Source: Baseline Survey, April 2009

In DCA 3, 80% of the farmers required animal husbandry related skills to increase profitability of the dairy
enterprise as shown in Table 57 below.




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Table 57: Farmers who need skills to increase profitability of dairy enterprise in DCA 3
                            Kisii                   Nandi Trans                             Uasin
 District         Bomet     Central Nyamira North Nzoia Bungoma                      Lugari Gishu   Nakuru Total
 Disease control      10%          5%        10%      17%       14%            7%        6%      6%   11%     9%
 Record keeping        3%          0%         6%       5%       12%           14%        3%      0%    0%     4%
 Milk handling         3%          0%         6%       5%        0%           10%        7%      4%    2%     4%
 Husbandry            85%        75%         78%      74%       74%           55%      83%      90%   87%   80%
 None                  0%          0%         0%       0%        0%            6%        0%      0%    0%     2%
 Biogas                0%          0%         0%       0%        0%            8%        0%      0%    0%     0%
 Total              100%         80%        100% 100%          100%         100%      100%     100%  100% 100%
Source: Baseline Survey, April 2009



4.32 Types and Organization of Community Groups
The study found that community groups in the project area had diverse organizational, managerial and
enterprise skills. This suggests that SDCP should develop customized solutions to deal with new dairy
producer and trader groups, including co-operative societies to improve their operations within a sound
legal and business footing. The reason for existence of these groups is to meet economic and social
objectives.
The following were the common factors among these groups:
    a) Crop oriented groups
    b) Dairy oriented groups
    c) Trader oriented groups
    d) Social support groups – especially those dealing with HIV/AIDS


The study found that these groups financed their activities through member contributions such as
merry-go-rounds and monthly subscriptions. In some cases, a few groups also had dairy cows they
used to generate income while some received milk as in-kind contributions. The social organization of
the groups was diverse. While members of some groups were exclusively women from one
community, others had both men and women members from different communities.


While nearly all the groups in the focus group discussions kept financial records, only some groups
kept other records such as minutes. The leadership and management of the groups suggested that social
services department had had an input in these groups. This is because in nearly all cases, the groups
had elected officials and written constitutions. This gave the members voice on the management of
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their resources. SDCP should therefore continue working with existing groups but encourage
formation of community groups with focused objectives on dairy enterprise. This also suggests the
need to continue working closely with the social services department to improve governance of the
groups which is critical in ensuring sustainability. However, this is only possible when communities
organize themselves into groups for ease of management and follow their terms of registration. This
information will be critical in designing market-driven commercialization of milk production,
processing, and trading. While SDCP had trained many groups in DCA 1 on group dynamics and
farming as a business, they still needed skills to mobilize resources, build networks and improve
management and value addition.




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           Table 58: Results of FGD Analysis of Community Groups in Project Area

District           Group           Membership      Objectives                          Management                    Business                Record Keeping     Finances             Training Areas
                                                                                                                     Development
1. Kisii Central   Keumbu          10 men          a) Increase milk market             Decision making is through    The group has not       Members            Monthly member       They identified the need to be
                   Community       13 women        b) Improve living standards of      consensus among members       received any business   records and        subscriptions        trained in milk quality control
                   Milk Vendors                       members.                                                       training                minutes of                              and using testing kits.
                                                   c) Create employment                                                                      meetings
                                                      opportunities.
2. Nandi North     Aganwet CBO                     a) Improve the living conditions    The group has 7 committee     The group has not       They always        They have            Improved management and
                   (Nandi Ethnic                       and incomes of the members      members of which 5 are        received any training   make records       monthly meetings     animal husbandry to increase
                   Group)                          b) Collecting and marketing milk    male and 2 are female         in managing dairy       for the group      where they collect   milk production
                                                       for members                     Decision making is by         enterprise              activities         member
                                                                                       consensus of members in                                                  subscriptions
                                                                                       the location- Chemnoet
3. Trans Nzoia     Mbiria Self     14 men              Assist members to get dairy     Elections are held once per   The group is not        The group          Merry –Go-Round      Most farmers are not willing to
                   Help Group      22 women            animals                         year.                         trained in business     keeps the          Shs 50 monthly       pay for training because there
                   (Mixed                              Some members have cross                                       management              minutes of their   contribution per     are many NGOs giving hand-
                   group)           Turkana,           bred cows, others don’t have    Officials                                             meetings as the    member.              outs during trainings e.g SDCP,
                                   Luyhas, Teso,       cows and some don’t even        Antony Lussala –                                      only consistent      The
                                                                                                                                                                 registration       World Vision, NAYAP
                                   Luos                have farms.                     Chairperson                                           records            fee is Kshs 100
                                                       They have applied for           Francis Wafubora Vice-
                                                       funding but still awaiting      Chairperson
                                                       funds                           Fred Masika – Assistant
                                                       Other NGOs they have been       Secretary
                                                       networking some years back      Florence Nasimiyu –
                                                       is V1. V1 has been supplying    Treasurer.
                                                       them with seed of Caliandria    Jerida Wasike –
                                                       and Sesbania etc. it has also   Dorcas Nasimiyu –
                                                       been providing them with        Priscilla Nafula-
                                                       training on Agro-forestry.      Alphonse Wanyama
                   Koschin         4 men              Koschin means to agree or to       Decisions are made by       SDCP has facilitated      They keep        Merry-go-round       Most farmers expressed interest
                   Group           22 women           love one another.                  members                     training of group         records,         and dairy cows       in being trained on farming as a
                                                      To reduce poverty among            Budget is done by           dynamics and farming      documents                             business.
                                                      members.                           executive committee         as a business.            and minutes
                                                      It has one ethnic group            They have group                                       during
                                                      Kalenjins.                         constitution and            Future plans of group     meetings.
                                                      The group is registered in the     members know group          is to purchase a plot
                                                      ministry of social service.        objectives                  and expand farming as
                                                      They network with social           Meetings are done once      a business.
                                                      service and SDCP                   per month
                                                      SDCP has facilitated training
                                                      of group dynamics and
                                                      farming as a business.
                                                      Future plans of group is to

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District     Group        Membership     Objectives                         Management                     Business                Record Keeping     Finances            Training Areas
                                                                                                           Development
                                           purchase a plot and expand
                                           farming as a business.
             Gaa-Seiyot   40 women         Improve living standard of       Election is done after 2       SDCP has facilitated    They have          Registration fee    Managing the dairy farming as a
             Group                         members.                         years                          training on dairy       minutes on all     Kshs 200            business and how to increase
                                           Improve girl child education.    They meet weekly with          management, planting    their activities                       milk production were the skills
                          Kalenjin and     Engaged in business project      executive and the rest of      fodder and grass.                          Monthly             that most members felt they
                          Luyha            e.g dairy farming as a           members.                       Given 4 dairy goats                        contribution Kshs   needed.
                                           business, selling milk etc.                                                                                100 per member
                                           To assist orphans especially     They have by-laws and they
                                           PLWHA                            don’t network with any
                                                                            organizations apart from
                                                                            SDCP.
                                                                            Mary Bel – Chairlady
                                                                            Rosebela Kole – Secretary
                                                                            Ann Melly – Vice –Secretary
                                                                            Egla Sirowey – Treasurer
                                                                            Felustud Boem- Committee
                                                                            Emily Tirop – Committee
4. Bungoma   Board of     5 Men             Income generation.              The organization has a         Pasture production,     The records        The use dairy       Feed formulation and fodder
             Evangelist   27 Women          Promote agricultural            written constitution that      livestock production,   kept are           animals as          production
             Self Help                      production.                     acts to guide group            Crop production,        attendance         collaterals to
             Group                          Improve milk production and     activities; it shows                                   records,           secure loans        Education on milk handling,
                                            marketing                       schedules for activities,                              financial                              fund raising, trading in farm
                                            Buy dairy cows for members      explains disciplinary                                  records,           Merry–go –ground    produce, micro financing,
                                            in rotation.                    measurers and succession                               production         and members         mobilizing ,training and
                                            Assist orphans, widows and      of group membership. It                                records,           contribution        awareness creation
                                            people with HIV/AIDS.           guides in borrowing and                                minutes of the
                                                                            contribution, leadership and                           meeting, sales     The group makes     SDCP has offered trainings on
                                                                            roles of members                                       and purchases      milk as in kind     biogas production, livestock
                                                                                                                                   records and        contributions       production, pasture production,
                                                                                                                                   health records,                        resource mobilization and
                                                                                                                                   every decision                         leadership, value addition,
                                                                                                                                   made is                                disease control and record
                                                                                                                                   followed up.                           keeping.
5. Lugari    Inyange      9 Men             Mobilize funds to buy cattle                                                           They keep                              Dairy farmers have a big
             Women        15 Women          for members                                                                            records of AI                          problem in marketing. They
             Group                          Assist members to pay                                                                  services, sales                        claim markets are seasonal,
                                            school fees, social and moral                                                          and production                         during dry seasons markets are
                                            support                                                                                records.                               available and prices are high but
                                                                                                                                                                          in wet seasons there are no
                                                                                                                                                                          markets leading to a lot of
                                                                                                                                                                          losses as they don’t have
                                                                                                                                                                          coolants for preserving milk
                                                                                                                                                                          instead they dispose at a throw
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District         Group          Membership   Objectives                        Management                      Business                 Record Keeping      Finances               Training Areas
                                                                                                               Development
                                                                                                                                                                                   away prices.
6. Uasin Gishu   Transparent    25 members   Dairy farming of cows and sheep   Elections are done yearly       The group did not        Maintain the        They collect a         Transporter of milk unreliable
                 Sirende Self                Improve living conditions of      Members know by laws            have any management      minutes of their    monthly                Lack of stable market
                 Help                        members                                                           training, value          group meetings      subscription from      (unorganized market)
                 Waitaluk                    Plan to buy a vehicle to          .                               addition and livestock   as a record of      each member to         Lack of enough leadership skills
                                             transport milk.                   Officials                       management               the decisions       support group          and management
                                                                               John Njenga – Chairperson                                that they make.     activities.            They need training on value
                                                                               Joseph Macharia –                                                                                   addition and management of
                                                                               Secretary                                                                                           livestock
                                                                               Milcah Aumot – Treasurer
                                                                               Mary Wamboi – Committee
                                                                               Roselyne Kae – Committee
                                                                               Daniel Ng’ang’a-
                                                                               Committee.
                 Fusiee         16 members   Improve living conditions of      Started 25/06/2006              Lack of adequate         They get 5 litres   Registration fee is    Animal disease control
                 Widows Self                 members                           registered at social services   capital to increase      of milk per day     100/=                  They cack of enough feeds and
                 Help Group                                                    ministry. The group has also    animals and do           and project to      Share fee is 100/=     pastures for animals
                                                                               bank account                    paddocking               have 10 litres      Merry-go-round         They need management skills of
                                                                                                                                        by end of           among members          animals and market
                                                                               Officials                                                season              Members also           information
                                                                               Elizabeth Makhoka –                                                          have dairy cows.       They need training on dairy
                                                                               Chairlady                                                                                           goats
                                                                               Jane Ngara – Secretary                                                       .
                                                                               Ruth Weruga – Treasurer
                                                                               Niva Luvai – Committee
                                                                               Penina Wafula – Committee
7. Nakuru        Rongai         20 members   To improve the economic           Groups decisions are made       SDCP has facilitated     Minutes of          Lack credit            Feed conservation
                 livestock                   welfare of their members          by consensus                    training of group        group meetings      facilities with milk   Water conservation structures
                 management                  through dairy farming by                                          dynamics and farming     as the only         as collateral          Heat detection
                                             mobilizing resources from                                         as a business.           regular records
                                             members
           Source: Analysis of Focus Group Discussions, April 2009




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4.33 Size of the Groups
The group membership range from 16 to 53 farmers per group. This is an indication of the sophistication
of the farmers in commercial dairy production.
4.34 Registered Cows
The survey showed that only 2.3% of all the farmers in the project area have registered dairy animals. Of
the 18 animals registered, only 6 were in DCA 1 while 12 were in DCA 3 as shown in Table 59 below.
Table 59: Organizations registering cattle in DCA 1 and DCA 3
 If cattle are registered,              DCAs                  Total
 which organization?
                                DCA 1           DCA 3
 Breeders Association                   5                7            12
 Dairy Recording System                 0                4             4
 Kenya Stud Book                        1                1             2
 Total                                  6           12                18
Source: Baseline Survey, April 2009


Surprisingly, the largest number of dairy animals was registered in Kisii Central and Bungoma District
while none was registered in Bomet, Lugari and Uasin Gishu as shown in Table 60 below.

Table 60: Farmers with cattle registered with at least one association
                   Are your cattle registered
 District          with any association?
                   Yes           No              Total
 Bomet             0%           100%             100%
 Kisii Central     12%          88%              100%
 Nyamira           3%           97%              100%
 Nandi North       1%           99%              100%
 Trans Nzoia       2%           98%              100%
 Bungoma           6%           94%              100%
 Lugari            0%           100%             100%
 Uasin Gishu       0%           100%             100%
 Nakuru            1%           99%              100%
 Total             2%           98%              100%
Source: Baseline Survey, April 2009



4.35 Animal Health Management and Delivery

Foot and Mouth Disease (FMD) and East Coast Fever (ECF) are the most common diseases in
the project area according to 82% of the respondents in the study. Table 61 below compares the
reported incidences of common livestock diseases by DCA. It shows that 58.2% of the dairy
farmers in DCA 1 cited FMD compared to 62% of the respondents in DCA 3. As regards ECF,


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20.3% of the farmers in DCA 1 reported ECF as the most challenging disease compared to 22%
of the farmers in DCA 3.
Table 61: Three common livestock diseases reported in DCA 1 and DCA 3
 Common livestock diseases              DCA 1        DCA 3     Total
 Foot and Mouth Disease (FMD)              58%          62%            60%
 Lumpy Skin Disease (LSD)                   4%           5%             5%
 East Coast Fever (ECF)                    20%          22%            21%
 Black Quarter                              0%           1%             1%
 Babesiosis                                 1%           0%             0%
 Anaplasmosis                               1%           2%             2%
 Mastitis                                   4%           0%             2%
 Worms                                      3%           0%             1%
 Ticks                                      0%           0%             0%
 None                                       8%           7%             7%
 Diarrhoea                                  0%           0%             0%
 Pneumonia                                  0%           0%             0%
 Total                                    100%         100%           100%
Source: Baseline Survey, April 2009


Table 62 shows that the most common livestock disease reported by farmers in the project area was
FMD which was cited by 60% all the respondents except in Nyamira district where ECF was more
important. While these are unproven farmers opinions, they provide the “rumors” report usually
maintained by veterinary office and that forms the basis for further follow-up.
Table 62: Most common Livestock Disease by District
                              Kisii                  Nandi    Trans                          Uasin
  Disease            Bomet    Central    Nyamira     North    Nzoia    Bungoma      Lugari   Gishu    Nakuru     Total
 Foot and Mouth
 Disease (FMD)        100%       88%            1%     84%     45%           64%     76%       91%       11%      60%
 Lumpy Skin
 Disease (LSD)          0%         3%           6%      0%      9%            5%     13%        1%         2%      5%
 East Coast Fever
 (ECF)                  0%         0%       18%        15%     34%           27%     10%        6%       73%      21%
 Black Quarter          0%         0%           0%      0%      2%            1%       1%       1%         0%      1%
 Babesiosis             0%         0%           0%      0%      1%            1%       0%       0%         0%      0%
 Anaplasmosis           0%         0%           5%      0%      4%            0%       0%       0%         4%      2%
 Mastitis               0%         0%           3%      1%      4%            1%       0%       0%         9%      2%
 Worms                  0%         0%       10%         0%      0%            0%       0%       0%         0%      1%
 Ticks                  0%         0%           1%      0%      0%            0%       0%       0%         0%      0%
 None                   0%         8%       54%         0%      0%            0%       0%       0%         0%      7%
 Diarrhoea              0%         0%           0%      0%      0%            0%       0%       1%         0%      0%
 Pneumonia              0%         0%           1%      0%      0%            0%       0%       0%         1%      0%
 Total                100%      100%       100%       100%    100%           100%   100%     100%       100%     100%
Source: Baseline Survey, April, 2009


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 These findings suggest that SDCP should place equal emphasis on encouraging vaccination as well as
 control of livestock disease pests and vectors.

 4.35.1 Livestock types and classes most at risk
 In the opinion of farmers in the project area, pure breeds in general and Frieshians in particular were the
 breeds that were at the greatest risk of contacting diseases. The survey found that 30% of the farmers
 reported that Friesians were most at risk. Other breeds such Jerseys, Guenseys and Holsteins were reported
 to be at risk by 2-3% of the respondents.

 4.35.2 Cost of providing animal health care per herd per month
 This survey showed that on average, farmers in DCA 1 incurred Kshs 417 to secure animal health
 services compared to their counterparts in DCA 3 who incurred Kshs 428 to secure the same
 services as shown in Table 59 below. Once again, this suggests that there was better access to
 health services in DCA 1 compared to DCA 3 either because of proximity to roads or due to
 competition among service providers that led lower cost of services.

 Table 63: Cost of securing animal health services between DCA 1 and DCA 3 by District
                         DCA1                                          DCA3
                                      Repeat      Other                      Repeat Other
   District   Transport Time         services     costs Transport Time services costs
Bomet              174.0       17.3       135.7       0.0    328.3     10.6       2.4   2.5
Kisii Central       27.9       11.8          0.4 576.8        83.1    118.8     47.0    0.5
Nyamira            271.6      168.4          0.3      0.0     43.3    646.0       0.2   0.0
Nandi North          0.0        0.0          0.0      0.0     28.6      2.9 -40.1       1.9
Trans Nzoia        180.0       45.7       175.8       0.0    110.0    198.8       1.4   0.0
Bungoma             99.3       39.3       629.3       1.9    134.7     23.7 803.1       0.0
Lugari             102.6        0.0          0.0      0.0    135.8     28.4     20.9   11.9
Uasin Gishu        135.0      269.6       175.2       0.0    195.8    318.5 385.2      80.0
Nakuru              98.7        0.6          0.3      0.0    293.5      4.7       0.6   0.4
Total              129.1       67.3       119.7      61.2    145.8    164.6 121.8      10.9
  Source: Baseline Survey, April 2009

 Further analysis of the cost of providing animal health services showed that farmers in Nandi
 North incurred the least animal health related expenses which may also be an indication that they
 may be using alternative medicine to treat their animals when sick. Farmers in Bungoma District
 incurred the highest cost in securing animal health services.




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4.36 Employment Creation in Dairy Enterprises

This survey found that the average dairy farmer in DCA 1 in Trans Nzoia district employed 2 permanent
workers and 1.2 casual workers as shown in Table 64 below. However, the average farmer in DCA 3
employed 1.1 permanent workers and 1.4 casual workers.

Table 64: Permanent and casual employees in an average dairy farm by District in Project Area
                    DCA1                                    DCA3
                    Permanent           Casual              Permanent        Casual
 District           Employees           Employees           Employees        Employees
 Bomet                    0                  1.0                  0                1.0
 Kisii Central           1.0                 1.8                 1.1               1.0
 Nyamira                 1.0                 1.2                 1.0               1.0
 Nandi North             1.1                   0                 1.0               1.0
 Trans Nzoia             2.0                 1.2                 1.2               1.1
 Bungoma                 1.2                 1.3                 1.3               1.0
 Lugari                  1.0                 1.2                 1.1               1.4
 Uasin Gishu             1.0                 1.1                 1.1               1.9
 Nakuru                  1.1                 1.0                 1.0                0
 Total                   1.1                 1.3                 1.1               1.4
Source: Baseline Survey, April 2009


The study found that only 26.4% of the dairy farming households in the project area employed
permanent employees while 17.9% employed casual workers in their dairy enterprise. This means
that at least 73.4% of the dairy producing households depend on family labour to carry out dairy
activities. There was however wide diversity between the districts depending on the size of the
dairy herd. Table 65 below shows that the average household has 4 dairy animals and employs
one casual worker and one permanent employee. Whereas Bomet District has the highest herd
size of 5.69 dairy animals, it was Uasin Gishu District where households employed the largest
number of casual workers which averaged 1.71.




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                                                                               SDCP Baseline Survey Report

Table 65: Average Dairy Herd and Employees by District
                  Dairy       Permanent Casual
 District         Animals     Employees Employees
 Bomet                 5.69         1.00        1.00
 Kisii Central         4.50         1.12        1.00
 Nyamira               2.30         1.00        1.14
 Nandi North           4.23         1.05        1.00
 Trans Nzoia           3.84         1.39        1.25
 Bungoma               4.60         1.41        1.38
 Lugari                4.58         1.15        1.33
 Uasin Gishu           4.77         1.10        1.71
 Nakuru                4.02         1.13        1.00
 Mean                  4.25         1.18       1.33
Source: Baseline Survey, April 2009

4.37 Breed Distribution
Table 66 below shows that cross breeds are the most widely distributed dairy animals in DCA 1.
Nakuru District had the highest proportion of Friesians in DCA 1 followed by Lugari District and
Kisii Central respectively. Guernseys were found in Bungoma and Nyamira District respectively.

Table 66: Distribution of Dairy Breeds by District in DCA 1
District         Friesians Jerseys      Guernseys       Crossbreed   Local   Aryshire Total
                                                                                      herd
Bomet           4%          8%          1%              56%          31%     0%       100%
Kisii Central 41%           0%          1%              37%          13%     8%       100%
Nyamira         31%         16%         3%              31%          13%     6%       100%
Nandi North     0%          0%          0%              100%         0%      0%       100%
Trans Nzoia     3%          0%          0%              93%          0%      5%       100%
Bungoma         20%         0%          12%             19%          10%     40%      100%
Lugari          69%         1%          1%              26%          4%      0%       100%
Uasin Gishu     50%         4%          0%              35%          3%      8%       100%
Nakuru          96%         0%          2%              2%           0%      0%       100%
Source: Baseline Survey, April 2009

This pattern of breed distribution was also found in DCA 3 as shown in Table 67 below.




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Table 67: Distribution of Dairy Breeds by District in DCA 3
District         Friesians Jerseys Guernseys Crossbreed Local Aryshire Total herd
Bomet                 35%        0%            2%           54%  0%  8%      100%
Kisii Central         45%        9%            8%           12% 26%  0%      100%
Nyamira                3%        7%          10%            64% 12%  4%      100%
Nandi North           11%        0%            0%           78% 10%  0%      100%
Trans Nzoia           33%        5%            2%           47%  4%  8%      100%
Bungoma               17%        4%            0%           33% 33% 12%      100%
Lugari                27%        2%            6%           45% 20%  0%      100%
Uasin Gishu           50%        1%            3%           35% 12%  0%      100%
Nakuru                92%        0%            1%            7%  0%  0%      100%
Source: Baseline Survey, April 2009


This pattern is consistent in all the dairy breeds. The study found that cross breeds are the most
common types of cattle in the project area followed by Friesians while Guernseys were the least
common breed as shown in Figure 25 and Figure 26 below. This is because cross breeds are
handier than pure breeds and therefore more productive low intensity management system which
is characteristic of the project area.
Figure 24: Distribution of Dairy Cattle Breeds in the Project Area




Source: Author, Baseline Survey, April 2009


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To quantify these maps, Table 69 below shows the number of animals by breed and district in the
project area.
Figure 25: Map showing the Breed Distribution in the Project Area




Source: Author, Baseline Survey, April 2009

4.38 Herd Structure
To determine the average herd structure, we analyzed the proportion of households with classes
of livestock by district and by DCA. Table 68 below shows the proportion of households in DCA
1 with breeds and classes of livestock in all the districts in the program. These findings show that
cross breeds and Friesians were the most breeds across all classes of livestock namely heifer
calves, mature heifers, bulls, dry cows, lactating cows and dry cows.


The results also show that most households had more than one breed of livestock and class of
livestock and therefore would appear to have been counted twice.




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Table 68: Distribution of dairy structure by breed in DCA 1
                                        Kisii                 Trans                      Uasin             Nandi
 District                      Bomet    Central   Nyamira     Nzoia   Bungoma   Lugari   Gishu    Nakuru   Central
 Friesian Heifer calves           6%       24%       11%        2%       46%     82%       38%       82%         0%
 Friesian Heifers                 4%       24%        6%        0%       16%     36%       13%       91%         0%
 Friesian Bulls                   0%        3%        0%        0%       11%      9%        4%        2%         0%
 Friesian dry milking cows        0%       26%        0%        4%        8%     41%       13%       20%         0%
 Friesians cows in milk          10%       71%       39%        6%       43%     15%       30%       27%         0%
 Jersey Heifer calves             0%        0%        6%        0%        0%      5%        2%        0%         0%
 Jersey Heifers                   4%        0%        6%        0%        0%      0%        2%        0%         0%
 Jersey Bulls                     4%        0%        0%        0%        0%      0%        0%        0%         0%
 Jersey dry milking cows          4%        0%        0%        0%        0%      0%        9%        0%         0%
 Jersey cows in milk             31%        0%       17%        0%        0%      0%        2%        0%         0%
 Guernsey Heifer calves           6%        0%        0%        0%       35%      5%        0%        2%         0%
 Guernsey Heifers                 0%        0%        0%        0%        3%      0%        0%        2%         0%
 Guernsey Bulls                   2%        0%        0%        0%        5%      0%        0%        0%         0%
 Guernsey dry milking cows        0%        0%        0%        0%        5%      0%        0%        0%         0%
 Guernsey cows in milk            0%        3%        6%        0%       27%      0%        0%        5%         0%
 Crossbred Heifer calves         88%       38%       17%       10%       30%     50%       26%        0%        14%
 Crossbreed Heifers              63%       18%       11%       46%       11%     27%        2%        5%        66%
 Crossbreed Bulls                51%        6%        0%       32%       22%     27%        4%        0%        37%
 Crossbreed dry milking cows     43%       24%        0%       80%       19%     23%       55%        5%        80%
 Crossbreed cows in milk         71%       68%       28%       40%       38%     41%       47%        5%        54%
 Local Heifer calves             33%       15%        6%        0%       19%      0%        0%        0%         0%
 Local Heifers                   22%        3%        6%        0%       14%      5%        0%        0%         0%
 Local Bulls                     47%        0%        0%        0%        5%      0%        0%        0%         0%
 Local dry milking cows          39%       15%        0%        0%        5%      5%        6%        0%         0%
 Local cows in milk              37%       15%       11%        0%       22%     14%        6%        0%         0%
 Aryshire Heifer calves           0%        6%        0%       10%       86%      0%        0%        0%         0%
 Aryshire Heifers                 0%        6%        0%        0%       46%      0%        0%        0%         0%
 Aryshire Bulls                   0%        0%        0%        0%       32%      0%        0%        0%         0%
 Aryshire dry milking cows        0%       15%        0%        4%       24%      0%       15%        0%         0%
 Aryshire cows in milk            0%       18%       11%        6%       62%      0%       15%        0%         0%
Source: Baseline Survey, April 2009



Table 69 below shows the results of the same analysis in DCA 3.




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Table 69: Distribution of dairy structure by breed in DCA 3
                                        Kisii                 Trans                      Uasin             Nandi
 District                     Bomet     Central   Nyamira     Nzoia   Bungoma   Lugari   Gishu    Nakuru   Central
 Friesian Heifer calves          28%       97%        0%       18%        9%     17%       25%       48%        56%
 Friesian Heifers                54%       10%        1%        6%       22%      5%       13%       40%        38%
 Friesian Bulls                   7%       40%        0%        6%        2%      2%        4%        8%        14%
 Friesian dry cows               17%       12%        0%        3%       13%      7%        6%       24%        20%
 Friesians in milk               59%       65%        6%        9%        9%     20%       23%       64%        98%
 Jersey Heifer calves             0%       17%        1%        0%        9%      2%        1%        2%         0%
 Jersey Heifers                   0%        3%        5%        0%        0%      0%        4%        0%         0%
 Jersey Bulls                     0%       10%        0%        0%        0%      0%        0%        0%         0%
 Jersey dry cows                  0%        0%        1%        0%        0%      0%        0%        0%         0%
 Jersey in milk                   0%       10%       10%        0%       11%     10%        1%        2%         0%
 Guernsey Heifer calves           4%       17%        8%        0%        2%      0%        9%        4%         0%
 Guernsey Heifers                 0%        3%        1%        0%        0%      0%        4%        6%         0%
 Guernsey Bulls                   0%        7%        2%        0%        0%      0%        0%        0%         0%
 Guernsey dry cows                2%        0%        1%        0%        0%      0%        3%        0%         0%
 Guernsey in milk                 4%        8%       12%        0%        4%      0%       10%        2%         2%
 Crossbred Heifer calves         74%       17%       29%       65%       35%     29%       39%       58%         6%
 Crossbreed Heifers              78%        5%       22%       29%       15%      2%       42%       40%         6%
 Crossbreed Bulls                57%        7%        6%       32%        9%      7%       12%       24%         4%
 Crossbreed dry cows             35%       10%        4%       56%       43%     15%       28%       20%         4%
 Crossbreed in milk              74%       22%       80%       80%       67%     44%       62%       58%         8%
 Local Heifer calves              0%       48%       14%       12%        0%     39%       19%       22%         0%
 Local Heifers                    0%       12%        6%        0%        4%      0%       17%       14%         0%
 Local Bulls                      0%       35%        0%       12%        2%     10%       17%       10%         0%
 Local dry cows                   0%        2%        0%        0%        2%      5%       16%        2%         0%
 Local cows in milk               0%       25%       11%       15%        4%     44%       10%       16%         0%
 Aryshire Heifer calves           7%        0%        0%        0%        4%     15%        0%        0%         0%
 Aryshire Heifers                13%        0%        1%        0%       11%      5%        0%        0%         0%
 Aryshire Bulls                   9%        0%        0%        0%        0%      0%        0%        0%         0%
 Aryshire dry cows                2%        0%        2%        0%        2%      2%        0%        0%         0%
 Aryshire in milk                20%        0%        6%        0%       13%     15%        0%        0%         0%
Source: Baseline Survey, April 2009




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Table 70: Mean Number of Animals by Breed in DCA 3

 District        Friesians   Holsteins   Jerseys     Guernseys   Crossbreed   Local    Aryshire     Total
 Bomet                1.65        0.43       0.00         0.11         3.20    0.00        0.48       5.9
 Kisii Central        2.03        0.00       0.40         0.35         0.55    1.20        0.00       4.5
 Nyamira              0.07        0.00       0.17         0.25         1.63    0.31        0.10       2.5
 Nandi North          0.41        0.00       0.00         0.00         2.85    0.38        0.00       3.6
 Trans Nzoia          0.54        0.65       0.20         0.07         1.70    0.13        0.30       3.6
 Bungoma              0.51        0.00       0.12         0.00         0.98    0.98        0.37       3.0
 Lugari               0.65        0.46       0.07         0.26         1.83    0.80        0.00       4.1
 Uasin Gishu          1.82        0.92       0.04         0.16         1.92    0.64        0.00       5.5
 Nakuru               2.18        1.18       0.00         0.02         0.26    0.00        0.00       3.6
 Total                1.06        0.39       0.12         0.16         1.60    0.51        0.12       4.0
Source: Baseline Survey, April 2009


4.39 Cost of Buying Dairy Animals
The cost of dairy cow is the greatest constraint for farmers intending to invest in the enterprise.
Table 71 below shows that the average farmer in DCA 1 was paying Kshs 26,532 to acquire a
dairy cow while farmers in DCA 3 were paying an average of Kshs 26,643.

There is however a wide variation in the cost of buying a dairy cow between the districts in the
project area. As shown in Table 72 below Bomet District registered the lowest price of dairy
cows at about Kshs 14,290 in DCA 1 while Nakuru District has the highest mean price of Kshs
40,385.


However, in DCA 3, farmers in Kisii Central on average paid an average of Kshs 20,870 per cow
which was the least in the programme area while farmers in Nakuru District paid highest prices
for dairy animals with an average price of Kshs 38,222 means that dairy enterprise excludes low
income households.




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Table 71: Average Cost of buying a dairy cow at source in Kshs
  Average cost of dairy cow at source
  District        DCA1       DCA 3
  Bomet           14,289     23,138
  Kisii Central 27,941       20,870
  Nyamira         21,434     22,532
  Nandi North     23,475     28,833
  Trans Nzoia     29,489     26,558
  Bungoma         23,278     20,919
  Lugari          31,632     25,209
  Uasin Gishu     32,692     27,620
  Nakuru          40,385     38,222
  Total           26,951     25,983
Source: Baseline Survey, April 2009


4.40 Production System
One of the key interventions in SDCP is to increase the adoption of intensive dairy production
system. To determine the adoption rate of intensive production systems enumerators observed the
types of structures and feeding systems. In general, three production systems were observed
namely: zero grazing, semi zero grazing and extensive grazing system. The semi-zero grazing
system was one in which farmers enclose their animals at night and part of the day and graze for
them for remaining part of the day. Table 73 below shows the proportion of dairy farmers with
dairy production system in DCA 1. These results show that the highest proportion of dairy
farmers without farm structures were in Nandi North (75%), Uasin Gishu(65%) and Trans Nzoia
(70%) Districts. The results also show that districts in DCA 1 with the highest adoption rates of
the zero grazing technologies were Kisii Central (44%), Lugari(37%) and Bungoma (26%).




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Table 72: Dairy Production System in DCA 1
 District        No           Semi Zero   Zero         Total
                 Structures   Grazing     Grazing
 Bomet                  9%          91%       0%       100%
 Kisii Central        18%          38%        44%      100%
 Nyamira              21%          61%        18%      100%
 Nandi North          75%          22%         3%      100%
 Trans Nzoia          70%          30%         0%      100%
 Bungoma              52%          26%        22%      100%
 Lugari               32%          37%        32%      100%
 Uasin Gishu          65%          35%         0%      100%
 Nakuru                8%          90%         3%      100%
 Total                39%          50%        11%      100%
Source: Baseline Survey, April 2009
Table 73 below shows the proportion of dairy farmers with dairy production system in DCA 3.
The results show that the highest proportion of dairy farmers without farm structures were in
Nandi North (79%), Uasin Gishu(70%) and Bungoma (58%) Districts. The districts in DCA 3
with the highest adoption rates of zero grazing technology were Kisii Central(29%), Bungoma
(26%) and Lugari (15%).
Table 73: Dairy Production System in DCA 3
 District        No           Semi Zero      Zero       Total
                 Structures   Grazing        Grazing
 Bomet                 13%          85%          3%     100%
 Kisii Central         10%          61%        29%      100%
 Nyamira               41%          49%        10%      100%
 Nandi North           79%          21%          0%     100%
 Trans Nzoia           40%          49%        12%      100%
 Bungoma               58%          16%        26%      100%
 Lugari                21%          64%        15%      100%
 Uasin Gishu           70%          26%          4%     100%
 Nakuru                 4%          94%          2%     100%
 Total                 35%          53%        11%      100%
Source: Baseline Survey, April 2009




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4.41 Cost of Zero Grazing
The study found that 36% of the households in the project area had some structures that could be
described as zero grazing units. Whereas the average cost of constructing a zero grazing unit in
DCA 1 was Kshs 21,075 while that of DCA 3 was Kshs 16,000, the standard deviation of the
costs in the two areas was not significantly different from zero as shown in Table 67 below. This
wide spread is explained by the fact that 64% respondents in both DCA 1 and DCA did not have
a zero grazing unit and therefore had spent nothing.


The survey found that the average cost of putting up a zero grazing unit in DCA 1 was Kshs 23,273
compared to Kshs 15,369 in DCA 3. However, this varied from Kshs 9,310 in Nyamira to Kshs 48,750 in
Lugari as shown in Table 74 below. On the other hand,
Table 74: Cost of zero grazing units in Kshs
Cost of Zero Grazing Structures
                 DCA1                          DCA3
District         Mean          Std.            Mean       Std.
                               Deviation                  Deviation
Bomet            15,000         .              12,500     3,535.5
Kisii Central 36,333           45,473.4        5,984      6,357.0
Nyamira          9,172         9,319.0         5,600       3,498.5
Nandi North      20,750        27,223.6        8,250      9,545.9
Trans Nzoia      20,400        27,718.6        23,143     25,863.1
Bungoma          11,375        16,611.7        12,332     17,333.3
Lugari           48,750        39,888.2        12,413     11,034.3
Uasin Gishu      44,185        28,050.0         23,500    13,448.3
Nakuru           20,543           16,161.6      27,667    26,341.3
Total            23,273         28,400.8       15,369     19,632.8
Source: Baseline Survey, April 2009

4.42 Farm Infrastructure
Other than the zero grazing units, the study also found that some of the farmers had also invested
in animal feed store at an average cost of Kshs 12,109. The average cost of constructing the feed
store was Kshs 13,226 in DCA 1 and Kshs 11,407 in DCA 3 as shown in Table 68 below.
However, the large standard deviation in the cost of these stores reflects the fact that 74% of the
respondents in the survey did not have feed stores and therefore had not incurred any cost in
setting it up.




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The cost of constructing feed stores was least in Bomet in DCA 1 and highest in Nakuru District
with an average cost of Kshs 22,200. The cost was dependant on the size, materials used and cost
of labour in the area and the costs varied shown in Table 75 below.


Table 75: Cost of other farm infrastructure in Kshs across the districts
DCA        District      Cost of         Cost of          Cost of    Total
Area                     feed store      milking shed     crush
DCA1       Bomet               742            485            975        2,202
           Kisii Central     4,784          8,667          6,792       20,242
           Nyamira           7,125          2,200                       9,325
           Nandi North       5,000          1,875                       6,875
           Trans Nzoia       9,000          6,000          1,392       16,392
           Bungoma           5,167          4,182          1,838       11,186
           Lugari           16,750                                     16,750
           Uasin Gishu      16,635        15,439           4,628       36,702
           Nakuru           22,200         1,629           1,167       24,996
           Total            11,849         4,406           2,728       18,984
DCA3       Bomet             3,621         2,488             679        6,788
           Kisii Central     1,594         2,002           1,331        4,928
           Nyamira           3,833         1,748           1,500        7,081
           Nandi North       5,640         1,686                        7,326
           Trans Nzoia      15,955         4,500           1,716       22,170
           Bungoma           3,250         2,627           2,000        7,877
           Lugari           10,013         3,350           1,150       14,513
           Uasin Gishu       7,458         7,500           2,940       17,898
           Nakuru           23,188         2,207           2,000       27,394
           Total            10,298         2,499           1,431       14,229
Source: Baseline Survey, April 2009


4.43 Cost of Labour
The cost of labour is an important consideration in commercial dairy enterprises. Table 76 below shows
that farmers in DCA 1 incurred a monthly wage bill of Kshs 2,625 for permanent employees compared to
Kshs 2,058 in DCA 3.

Table 76: Monthly wage bill for permanent employees between DCA 1 and DCA 3
 DCAs      Minimum      Maximum       Mean       Std. Deviation
 DCA 1        600.00    12,000.00     2,625.37         1,656.06
 DCA 3        700.00      7,000.00    2,058.39          1,016.78
 Total         600.00 12,000.00       2,239.28          1,280.09
Source: Baseline Survey, April 2009



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Due to seasonal and daily variations in labour requirements in the dairy enterprise, most farmers
preferred to hire casual labour. Table 77 below shows that the average monthly wage bill on
casual labour incurred by farmers in DCA 1 was Kshs 2,257 compared to Kshs 2,006 incurred by
farmers in DCA 3.

Table 77: Monthly wage bill for casual employees between DCA 1 and DCA 3
 DCAs       Minimum     Maximum          Mean           Std. Deviation
 DCA 1           100        6,300        2,256.86            1,682.410
 DCA 3             70       15,000       2,005.71           1,902.920
 Total            70       15,000        2,095.92           1,824.743
Source: Baseline Survey, April 2009


Further analysis of the cost of labour indicated that it varied widely across the project area with
Kisii Central having the least monthly wage costs of Kshs 2,453 whereas Nakuru District had the
highest monthly wage bill of Kshs 5,525 as shown in Table 78 below. This variation is partly due
to prevailing employment opportunities and the prevailing wage rates.
Table 78: Average Monthly Wages in Kshs
                 Monthly wage bill    Monthly wage          Total
                  for permanent       bill for casual      Monthly
 District           employees          employees           Wage Bill
 Bomet                       600                2,012             2,612
 Kisii Central              1,853               600               2,453
 Nyamira                    1,691              1,800              3,491
 Nandi North                1,645               600               2,245
 Trans Nzoia                2,187              1,494              3,681
 Bungoma                    2,667              2,394              5,061
 Lugari                     2,414              2,212              4,626
 Uasin Gishu                2,625              2,594              5,219
 Nakuru                     2,625              3,000              5,625
 Total                      2,239              2,096              4,335
Source: Baseline Survey, April 2009

4.44 Condition of Milking shed
The condition of the milking shed is one of the critical infrastructure in ensuring clean milk
production. Table 79 shows that it was only 25% of the farmers in DCA 1 who had a zero grazing
unit in good condition compared to 30% in DCA 3. However, the zero grazing units in fair
condition were 62% in DCA 1 compared to only 49% in DCA 3. Again this demonstrates the
progress made by farmers in DCA 1 as a result of the investment in training that they had
received in the last two years.

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Table 79: Condition of zero grazing unit between DCA 1 and DCA 3
                 Condition of zero grazing unit               Total

 DCAs        Good       Average             Poor
 DCA 1            31            77              15
                                                                  123
                25%           62%             13%
 DCA 3            56            81              29
                                                                  166
                33%           49%             18%
 Total            87           158              44
                                                                  289
                30%           55%             15%
Source: Baseline Survey, April 2009


This study showed that 30% of the dairy farmers in the project area had a milking shed of one
form or another. Of these milking sheds, 30% were in good condition, 55% were in average
condition and the remaining 15% were in poor condition as shown in Table 80 below.

Table 80: Condition of milking shed by District
 District               Condition of milking shed                 Total
                    Good        Average           Poor
 Bomet                     3          24                  1               28
 Kisii Central           25            14                 5               44
 Nyamira                   0           45                 1               46
 Nandi North             11             1                 0               12
 Trans Nzoia               7            8                 4               19
 Bungoma                 11            14                 0               25
 Lugari                    4            4                 1               9
 Uasin Gishu             11             6                 1               18
 Nakuru                    5           27                28               60
 Total               77           143                    41             261
Source: Baseline Survey, April 2009

4.45 Gender in Dairy
The survey found that 30% of the households were female headed as shown in Figure 27 below.




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Figure 26: Gender of the household head




Source: Baseline Survey, April 2009

Table 81 below shows that there 32% of the households in DCA 1 were female headed compared
t0 28% of the households in DCA 3.
Table 81: Gender of household head in DCA 1 and DCA 3
         Gender of household head      Total
 DCAs       Male          Female
 DCA 1           236             112
                                          348
              67.8%            32.2%
 DCA 3           321             126
                                          447
              71.8%            28.2%
 Total           557             238
                                          795
                70%             30%
Source: Baseline Survey, April 2009

However, there was wide diversity with Bungoma having the highest proportion of female
headed households than male headed households while Bomet District had the least as shown
below. This was unexpected and may suggest that female headed as shown in Figure 28 below.




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Figure 27: Gender of the Household Heads by District




4.46 Gender Division of Labour
The study team identified a typical man and a typical woman from among the focus group
discussion participants. These individuals were selected based on their willingness to share
information without being egocentric and average status in the community. In identifying a
potential respondent, the moderator would ask members of the focus group to suggest individuals
that fitted these criteria. However, to reduce potential sources of bias, this information was also
triangulated with feedback from other interviews. It is these two that filled the daily activity
schedule which outlined the tasks they perform on a typical day from the time they wake up to
the time they sleep at night. The daily activity schedule also sought to obtain information on the
location of the tasks and whether or not the men and women were paid for the tasks.


Analysis of the daily activity schedules for men and women in the study areas revealed that
women and men perform reproductive roles, i.e. roles pertaining to the care and maintenance of
the household and its members e.g. sweeping, construction of houses, mending fences, cleaning
pens, food preparation, fetching water and firewood, washing dishes, caring for children and the
sick and elderly etc. The other role that men and women are involved in is productive activities
undertaken to get an income in cash or kind. They also undertake community roles which
involves the collective organization of social events and services. They include ceremonies and
celebrations, community improvement activities, participation in local groups and organizations,
harambee projects, collective agricultural activities, political parties, church groups etc.
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The data collected showed that a woman’s day begun at 5.00 a.m. and ended between 10.30 and
11.00 p.m. The men on average woke up at 5.30 and retired at 10.00 p.m. Table 82 below outlines
the tasks performed by men and women in the study areas.
Table 82: Comparison between men and women roles in dairy producing households

       Tasks performed by Women                               Tasks performed by Men
 1. Preparing the milking equipment                  1.  Taking milk to the collection centre
 2. Milking                                          2.  Cutting grass or fodder
 3. Preparing breakfast                              3.  Feeding dairy cows
 4. Preparing children for school                    4.  Weeding napier grass
 5. Washing utensils                                 5.  Watering the dairy animals
 6. Cleaning the house                               6.  Taking a nap or siesta
 7. Collecting fodder                                7.  Taking a walk
 8. Feeding the cows                                 8.  Tethering the animals
 9. Cleaning the cow shed                            9.  Watching the news
 10. Taking milk to the collection centre            10. Plucking tea leaves
 11. Preparing lunch                                 11. Cleaning the cow sheds
 12. Weeding napier grass                            12. Checking on the animals
 13. Work in the shamba                              13. Taking tea leaves to the buying centre
 14. Fetching firewood                               14. Going to the shopping centre to have a
 15. Fetching water                                      chat
 16. Washing clothes                                 15. Visiting neighbours to socialize
 17. Preparing supper                                16. Removing stumps
 18. Assisting children with their homework          17. Looking for wage employment
 19. Preparing children to go to sleep
Source: Analysis from FGDs, April 2009

From the Table 35, it is evident that women are overburdened by reproductive roles and this may
have a negative impact on their health status and on their effective participation in dairy
production. The analysis also revealed that whereas men had free time for a nap or siesta, to take
a walk and visit neighbors to socialize, women’s typical day was fully occupied with no time to
rest or for leisure activities. It is no wonder that women find it difficult to effectively participate
in community roles such as farmers cooperatives or to take up leadership roles in such
associations.
Analysis of the location (from the house) of the tasks performed by men and women revealed that
most of the tasks and responsibilities borne by women are performed within the homestead. The
exception was fetching firewood, fetching water, taking milk to the collection centre. These tasks
took place between 2-3 kms away from the homestead. On the other hand, the tasks performed
by men were mostly located away from the homestead and the distances ranged from 200 meters
to 3 kilometers.
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Apart from the daily activity schedule, a more detailed analytical tool was developed to capture
the tasks performed by men and women in dairy milk production. The tool also sought to find
out how rigid the gender division of labour was in relation to specific tasks. Table 83 below
shows the findings.
Table 83: Gender division of labour in dairy producing households
Dairy task                          Performed by              Task performed       How rigid is the
                                                              mainly by gender     division of labour
1) Napier grass management          Both men & women                 Men                 Flexible

2)   Crop residue harvesting        Both men & women              Men                     Flexible
3)   Fodder conservation            Both men & women              Men                     Flexible
4)   Spraying and disease control   Men                           Men                      Rigid
5)   Artificial Insemination        Men                           Men                      Rigid
6)   Feeding dairy cows             Both men & women        Both men &                    Flexible
                                                            women
7) Watering the animals             Both men & women        Both men &                    Flexible
                                                            women
8) Grazing animals                 Men                            Men                      Rigid
9) Treatment of sick animals       Men                            Men                      Rigid
10) Milking                        Women                         Women                     Rigid
11) Milk Marketing                 Both men & women               Men                     Flexible
12) Cleaning sheds                 Both men & women              Women                    Flexible
13) Milk processing                Both men & women               Men                     Flexible
14) Management of hired labour     Both men & women               Men                     Flexible
Source: Baseline Survey, April 2009

The tasks that are predominantly done by men are: spraying animals and disease control,
organizing or facilitating artificial insemination and treatment of sick animals. These tasks are
technical and require input from extension service providers. This is not surprising as the women
interviewed noted that one of the resources they do not have access to and control over is
extension services.
Women’s participation dominated in milking and cleaning of the cow sheds in all communities
except among the Kipsigis community. Other tasks were performed by both men and women as
the division of labor was flexible. Milk marketing was categorized into two i.e. local sales and
sale to cooperative society but the daily activity schedule revealed that it is the men who took the
milk to the collection centers.


Another tool used to capture the gender dynamics in the study area was the access and control
profile. This tool aimed at analyzing the resources men and women had access to and control

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over. Access was defined as the opportunity to make use of something while control was defined
as the ability to define its use. The resources included land, dairy cows, education, extension
services, credit facilities, labour, equipment, income, assets, health services, child care, trees,
cattle, household goods, labour and time, The study revealed that although women had access to
most of the resources mentioned, they often had limited or no control over the same resources.
The results show whereas men had full access to all the resources listed, women had full access to
all the resources apart from credit, income, milk, and extension services which they only had
partial access to. Farm machinery was also cited as a resource that women have no access to and
the reason given was that the machinery belong to men and that women were not trained to
operate them.


An examination of the control profile revealed that although women may have full or limited
access to a number of resources they rarely have control over the same resources. The study
showed that women have partial control over all resources listed except extension services,
income, land, dairy cows and farm machinery.


Land is one key resources that is controlled by men. This is not surprising since Kenyan societies
are patriarchal and gender relations are such that it is the woman who joins her husband in
marriage. This means that it is men (sometimes with no consultation) who make decisions on
land use e.g. how much land will be put to agricultural production. Women’s lack of control over
land has serious implications also on their access to credit facilities since financial institutions
require some form of collateral before approving any loan application.


Other resources that are controlled by men included income, decision making power and dairy
cows. Some women controlled the income from the milk sold locally (mainly to neighbours)
while the men controlled the income from sale of milk to the cooperative society and other
institutions. This has serious implications on the dairy production and women’s participation in
the sector. With limited incomes women’s access to farm inputs and hired labour is also
decreased. Another challenge is that when the man is the decision maker and he is away most of
the time e.g. due to rural urban migration, decisions are delayed and production is affected.


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Extension services are very important in improving milk production. Despite the fact that there is
a correlation between extension services and overall performance of the dairy farms, the study
revealed that this is one resource that women have limited or no control over. While women do
most of the activities involved in dairy production extension and training services target men. The
following section briefly outlines the challenges of integrating gender issues in each of the main
communities.
4.47 Savings and Credit
Income is the most important factor in determining savings behavior among poor households.
The factors that motivate households to save are common among both the affluent and poor
households. Indeed, the precarious socio-economic conditions under which they operate dictate
that they should have a higher inclination (propensity) to save. This survey showed that on
average 34% of the households have at least one person who saving.


Table 84 below shows the proportion of households making regular savings from the dairy
enterprise in DCA 1. It shows that Bomet (11%), Uasin Gishu (15%) and Trans Nzoia (17%)
Districts had the lowest proportion of smallholder dairy households that saved regularly. On the
other hand, Bungoma(67%), Nakuru(46%) and Kisii Central(38%) had the highest proportion of
dairy households that were saving regularly.
Table 84: Households making regular savings from the dairy enterprise in DCA 1
                     Kisii               Nandi       Trans                              Uasin
 District Bomet Central Nyamira North                Nzoia Bungoma Lugari               Gishu      Nakuru     Total
 Yes        11%      38%      34%        34%         17%       67%            37%       15%        46%        31%
 No         89%      62%      66%        66%         83%       33%            63%       85%        54%        69%
 Total      100%     100%     100%       100%        100%      100%           100%      100%       100%       100%
Source: Baseline Survey, April 2009

Table 83 below shows the proportion of smallholder dairy households making regular savings
from the dairy enterprise in DCA 3. Trans Nzoia (19%), Bungoma(24%), Nyamira(28%) and
Lugari Districts(28%) had the least proportion of households making regular savings from the
dairy enterprise. Lugari, Kisii Central and Nandi North on the other hand had the highest
proportion of dairy households that were saving regularly.




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Table 85: Households making regular savings from the dairy enterprise in DCA 3
                   Kisii                Nandi      Trans                                  Uasin
 District Bomet Central Nyamira North              Nzoia         Bungoma Lugari           Gishu       Nakuru Total
 Yes       40%     47%       24%        43%        19%           28%           28%        56%         31%           35%
 No        60%     53%       76%        57%        81%           72%           72%        44%         69%           65%
 Total     100%    100%      100%       100%       100%          100%          100%       100%        100%          100%
Source: Baseline Survey, April 2009



Figure 28: Distribution of the Households making Savings




Source: Analysis of the Survey, April 2009




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Figure 29: Preferred Mode of Saving




Source: Baseline Survey, April 2009


Figure 30: Preferred Methods of Savings




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Table 86 below shows where household members make their savings in DCA 1 and DCA 3.
Table 86: Comparison between DCA 1 and DCA 3 in terms of where HH member make their savings
  Where is the HH          DCAs           Total
 member making
 their savings?
                      DCA 1     DCA 3
 Local trader             6        15        21
 Group                     23       39       62
 Cooperative                6       20       26
 Savings account           78       82     160
 Home savings               1        3        4
 Total                    114       159    273
Source: Baseline Survey, April 2009



Poor households save for a different reasons, among them the following:

    a) Emergencies and investment opportunities that may arise any time
The poor, with no access to insurance services and cheap and readily accessible sources of short-
term finance, have a high need for savings to take care of any emergencies or investment
opportunities that may arise any time.
   b) Saving for Consumption
Households with uneven income streams e.g. dairy farming with its seasonal variations save for
consumption during the periods in which income is low. Indeed, there is abounding empirical
evidence to show that many poor people who frequently require food, medical or other life-saving
relief services normally find themselves under such conditions because they lack savings
opportunities which would have enabled them to put aside part of their past income flows to help
them when rains fail or disaster strikes.
   c) Saving for investment
Households have investment needs which, given the scarcity(and, sometimes, even
undesirability) of credit facilities, must be financed through their own savings. For instance,
households may save for their children's education (investment in human capital), house
construction, electrification, purchase of plots, among many other possible investment needs. In
enterprise development, studies have indicated that individual savings are the principal source of
start-up capital and enterprise expansion.




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    d) Saving for social and religious purposes
People of various cultural, social and religious backgrounds may save for certain occasions that
require large amounts of expenditure e.g. religious festivals, weddings, funerals, the purchase of
consumer durables e.g. car, TV etc.
   e) Saving for retirement, ill health or disability
Some people save for anticipated or unforeseen, but likely events, when their income streams will
be low or non-existent. Appropriately designed savings products could substantially generate
savings to take care of the health, education and other basic needs of their children in the future
once their health deteriorates or after they die.
    f) Use of savings to leverage other financial services (e.g. credit)
There are many households/individuals who save for the purpose of building up a collateral base
to enable them to obtain credit. This is particularly important in savings and credit associations
(formal & informal) which are quite popular among the poor. Such savings enable the poor to
obtain loans to start their own small businesses, finance the education of their children, pay
medical bills or meet other financial needs of the household.
   g) Savings as a way of minimizing irresponsible spending
Many people save money for the sake of converting it into a form that is not easily accessible for
irresponsible spending e.g. drinking, shopping sprees, assisting undeserving relatives/friends etc.
Extent of savings and credit use by individuals and groups, loan amounts, interest charged,
application of funds borrowed and repayment period
4.48 Loan Applications
Out of the 795 dairy farmers interviewed, it was only 147 who had applied for a loan in the
previous year or 18.5% of the population. Figure 33 below shows that most of the applications
were made in the month of January, March and August. These periods are one month before the
opening of schools when most parents seek credit to pay school fees. January is particularly
critical because most schools require new uniforms and books as students move to new classes
thus occasioning high cash education related requirement.




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Figure 31: Loan Application by Month




Source: Baseline Survey, April 2009

Table 87 below shows that 3.8% of the applicants in DCA 1 were able to access credit compared
to 5.2% of the applicants in DCA 3. The difference is statistically insignificant and therefore no
difference between access to credit in the two areas.

Table 87: Access to credit in DCA 1 and DCA 3
        Was the application successful?
              District          DCA1      DCA3
Yes          Bomet                    2%      30%
             Kisii Central          18%       10%
             Nyamira                39%        0%
             Nandi North              3%      11%
             Trans Nzoia            17%       14%
             Bungoma                44%       16%
             Lugari                 47%       31%
             Uasin Gishu              0%      58%
             Nakuru                 10%        7%
                                    17%       19%
No           Bungoma                  7%       0%
             Lugari                   0%      16%
             Uasin Gishu              0%       3%
             Nakuru                   0%       3%
Source: Baseline Survey, April 2009




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Purpose of Borrowing
Figure 33 below shows that financing education was the most common reason why smallholder
dairy farmers borrowed accounting for for 28% of all the purposes. Borrowing to finance dairy
farming activites such as buying feeds, constructing zero grazing units, fencing etc and house
repairs were the least common reasons for borrowing and accounted for less than 1% of the
reasons given for applying for loans. On the other hand, 22% of all borrowers used the funds to
procure dairy cattle. This is consistent with the finding that cost of dairy cows was high.


Non-business uses accounted for the 26% of the purpose for which farmers borrowed money. The
main uses for which households aquired credit were to buy food, pay school fees and to pay for
health care.
Figure 32: Reasons why farmers borrowed the previous season




Source: Baseline Survey, April 2009

4.49 Type of Lender
Figure 34 below shows the type of lenders from whom farmers applied for loans in the program
area. The analysis showed that micro-finance institutions are the most important sources of credit
in the program area accounting for 34% of all the loan applications followed by cooperatives and
commercial banks which accounted for 24% and 15% of the applications respectively. The least
important sources of credit are relatives and the settlement fund each of which accounts for less
than one per cent each.




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Figure 33: Type of Lender




Source: Baseline Survey, April 2009
4.50 Loan Products
To determine the loan products available in the program, the key features of the loans disbursed
were analyzed separately namely: a) Loan size, b) Grace period c) Loan repayment period and d)
Loan repayment intervals and e) Interest charges.

4.50.1 Loan Size
Analysis of the loan size in the program area showed that 50% of all the loan applications were
less than Kshs 30,000 and 75% were less than Kshs 70,000. This loan size suggests that majority
of small holder dairy farmers seek credit to meet short term cash requirements rather than for
investment because the average repayment period was 13.4 months.


Analysis of the loanees showed that only 50 loans that were disbursed in DCA 1 compared to 90
loans in DCA 3. The average loan size in DCA 1 was Kshs 61,834 while in DCA it was Kshs
58,272 as shown in Table 88 below.




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Table 88: Loan Size in Kshs
                              Amount received in Kshs
DCA Area District           Minimum     Maximum         Mean       Std.
                                                                   Deviation
DCA1        Bomet                15,000       15,000        15,000      0.0
            Kisii Central         5,000      150,000        67,833      55,661
            Nyamira               5,000       40,000        22,667       7,761
            Nandi North          15,000       15,000        15,000      0.0
            Trans Nzoia          20,000      160,000        75,125      49,412
            Bungoma              15,000      300,000        74,142      83,920
            Lugari                3,000      500,000        90,111    156,506
            Nakuru               20,000      150,000       105,000      61,373
            Total                 3,000      500,000        62,477      81,309
DCA3        Bomet                 5,000      110,000        37,792      34,993
            Kisii Central         5,000      150,000        42,000      54,845
            Nandi North           5,000       40,000        23,000      13,509
            Trans Nzoia          20,000      250,000        89,500      83,484
            Bungoma              20,000      140,000        50,000      40,723
            Lugari                5,000      350,000        88,714      99,280
            Uasin Gishu          10,000      150,000        43,552      33,463
            Nakuru               50,000      161,000       102,750      61,076
            Total                 5,000      350,000        58,272      64,175
Source: Baseline Survey, April 2009


4.50.2 Success Rate
To determine the adequacy of the credit in the SDCP program area, we analyzed the success rate
of loan applicants and found that 50 out of loan 52 applicants in DCA 1 successfully for a loan
compared to 90 out of 95 applicants in DCA 3 as shown in Table 89 below. This suggests that
credit supply is not a serious constraint for majority of smallholder dairy farmers in the SDCP
program.




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Table 89: Success rate in DCA 1 and DCA 3
                     DCA Area
District                 DCA1   DCA3   Total
Bomet            Yes       100%   100%  100%
Kisii Central    Yes       100%   100%  100%
Nyamira          Yes       100%         100%
Nandi North      Yes       100%   100%  100%
Trans Nzoia      Yes       100%   100%  100%
Bungoma          Yes        86%   100%   90%
                 No         14%     0%   10%
Lugari           Yes       100%    88%   91%
                 No          0%    13%     9%
Uasin Gishu      Yes                  97%   97%
                 No                    3%    3%
Nakuru           Yes        100%      80%   89%
                 No           0%      20%   11%
Source: Baseline Survey, April 2009

In general, this study showed that 95% of all the applications were successful as shown in Figure
35 below.
Figure 34: Loan Success Rate




Source: Baseline Survey, April 2009

4.50.3 Reasons for Unsuccessful Loan Applications
To understand the reasons why 5% of all the applicants failed to get loans, we analysed the
reasons for the failed applications. The analysis showed that all the unsuccessful applicants in
DCA 1 had been late to apply for the loans as shown in Table 90 below.


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Table 90: Reasons for unsuccessful loan applications in DCA 1 and DCA 3
                Why was the application
 District       not successful?                DCA1 DCA3
 Bungoma        Late loan application          100%      0%
                Did not meet requirements        0% 33%
 Lugari
                Could not get guarantors         0% 33%
                Late loan application            0% 33%
 Uasin Gishu Late loan application               0% 100%
 Nakuru         Complicated procedures           0% 100%
Source: Baseline Survey, April 2009


The most common reason for unsuccessful application was late loan application which accounted
for 57% of all unsuccessful applications as shown in Figure 36 below. In addition, 14% could not
get guarantors while 15% did not meet requirements. These are some of the conditions that
cooperative impose to safeguard the members interest and it confirms that most of unsuccessful
applicants were farmers seeking credit from cooperatives.

Figure 35: Reasons for unsuccessful loan applications




Source: Analysis of the Baseline Data, April 2009

4.50.4 Type of Payment
This study showed that 94% of all theloans were paid in cash and that only 5% were paid in kind
especially those secured against future milk deliveries as shown in Figure 36 below. This
suggests that there is wide scope to avail credit to farmers against milk deliveries in the SDCP
project area because this avenue for credit delivery has not been exploited.


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Figure 36: Type of Payment




Source: Baseline Survey, April 2009
4.50.5 Loan Repayment Period
This survey found that farmers in DCA 1 had a mean repayment period of 12.24 months
compared to 14.09 months in DCA 3 as shown in Table 91 below which confirms that short term
borrowing is the predominant type of credit sought and disbursed in the project area. It is only 8%
of the successful applicants who received loans that were repayable in periods of 2 or more years.
Table 91: Repayment period (months) in DCA 1 and DCA 3
 DCA Area         N         Minimum      Maximum        Mean      Std. Deviation
 DCA1                 50           2           36         12.24            6.096
 DCA3                 90            2              48     14.09           7.971
 Total               140            2              48     13.43           7.388
Source: Baseline Survey, April 2009


4.50.6 Interest Rate
There was a wide spread of the interest rates that were charged on loans in the project area
depending on the lender, the amount and the time charged for the loan. In general, the average
annual interest rate on loans in DCA 1 was 28.5% compared to 18.6% in DCA 3 as shown in
Table 92 below.
Table 92: Interest rate (p.a) in DCA 1 and DCA 3
 DCA              N         Minimum      Maximum        Mean      Std. Deviation
 DCA1                 50         3.00      300.00       28.5200        45.04974
 DCA3                 90         1.50       120.00      18.6089       15.75193
 Total               140          1.50      300.00      22.1486       29.95018
Source: Baseline Survey, April 2009


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Analysis of the interest rate charged against loans showed that farmers who took smaller loans of
between Kshs 3,000 – Kshs 20,000 paid the highest interest rate of 87% while the ones who took
large loans from Kshs 150,000 to 350,000 paid the interest rate of only 16% as shown in Figure
38 below. This suggests that the high interest rates are partly to cover the high transaction and
operating costs of small loans. Even at the bottom of the income pyramid, very poor borrowers
active in petty trade or selling goods repay rapidly thanks to the very high margins and turnover
of their income-generating activity. In short, the borrowers targeted by microfinance activities
should not be responsive to price changes below very high levels of interest rates. There is
however very little data on the returns on investment of the poor, and the importance of interest
rate payments for them.


Figure 37: Mean Loan Size and Interest Rates




Source: Baseline Survey, April 2009


This survey showed that that 75% of all the loans were disbursed at an interest rate of 23% or
less. Contrary to experiences in other studies this survey showed that money lenders in the project
area charged relatively modest interest rates of 17% compared to AFC which charged 27.4% and
Chamas which charged 27. 3% higher interest rates as in Table 93 below.




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Table 93: Interest rates (%) charged by type of lender
                                                                     Std.
 Type of lender            Minimum       Maximum         Mean      Deviation
 Relative                      12.00         12.00       12.0000               .
 Money lender                     8.00       29.00       17.2727     6.60441
 Cooperative                      3.00      300.00       25.3333    48.62568
 AFC                              6.00       75.00       27.4000    24.52754
 Settlement fund                  5.00        5.00        5.0000               .
 Chamas (ROSCAS)                 10.00      143.00       27.3333    43.63198
 Others lenders                   4.00       20.00       10.0000     7.11805
 Commercial bank                  1.50      120.00       21.1571    23.58471
 MFI                              4.00       90.00       20.7979    12.62617
Total                           1.50        300.00       22.1486    29.95018
Source: Baseline Survey, April 2009



Table 94: Size and terms of loans in DCA 1 and DCA 3
                          DCA1                                                     DCA3
                                    Payment Interest                                 Payment Interest
                    Purpose of      period    rate         Purpose of                period    rate
 District           borrowing       (months) (p.a)         borrowing                 (months) (p.a)
                    Education            12.0    4.0%      Education                        12    24.1%
 Bomet
                    Total                12.0    4.0%      Non business                     12      27%
                                                           Buy land                         24      10%
                                                           Maize planting                   12      10%
                                                           Total                            13    22.5%
                    Education               14        6.7% Education                        12       8%
 Kisii Central      Non
                    business                12        1.5% Non business                 11.25    13.25%
                    Buy cattle              12         32% Buy cattle                      12       10%
                    Total                   13        9.2% Total                         11.5     11.8%
                                                           Non business                     2      120%
                                                           Buy land                         6       27%
                                                           Buy cattle                      12     14.3%
 Nandi North                                               Total                          8.8       38%
                    Non
 Nyamira
                    business                10       27.3%
                    Buy cattle            11.5       40.5%
                    Total                 11.2       37.9%
                    Education               11         24% Education                       22        11%
 Trans Nzoia        Non
                    business                12           8% Buy cattle                     12        20%
                    Maize
                    planting                12       45.5% Farm inputs                     12       17%
                    Dairy                   12         16% Total                        18.67     13.5%
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                      DCA1                                          DCA3
                              Payment Interest                        Payment Interest
                Purpose of    period   rate        Purpose of         period   rate
District        borrowing     (months) (p.a)       borrowing          (months) (p.a)
                farming
                Farm inputs          12      20%

                Total             11.63    25.9%
                Education          8.67    17.3% Education                   23        6%
                Buy cattle        14.25      16% Non business                11     15.2%
Bungoma
                Maize
                planting          16.67      28% Funeral                     12     10.8%
                House
                repairs               8      22% Maize planting              12       25%
                Farm inputs           9      20% Total                    14.71     13.3%
                Total              12.5    20.1%
                Non
Lugari          business             14    38.3% Education                 18.8     13.8%
                Buy cattle           12    23.7% Non business                12       20%
                Total             13.33    33.4% Buy land                    36        4%
                                                 Buy cattle                  22      8.7%
                                                 Ceremony                    21        8%
                                                 Funeral                     12       10%
                                                 Others specify            11.3     13.3%
                                                 Total                     18.6     12.1%
                                                 Education                 11.7       30%
 Uasin Gishu                                     Non business              12.3     19.2%
                                                 Buy land                    12       20%
                                                 Buy cattle                  10     22.7%
                                                 Maize planting            11.5      18. %
                                                 Farm inputs                 12       20%
                                                 Total                    11.69     22.1%
                Education            15      30% Education                   10       24%
Nakuru
                Non
                business             18      16% Non business                15     17.3%
                Buy cattle            6      22% Total                     13.8       19%
                Total             14.25      21%
Source: Baseline Survey, April 2009

4.50.7 Type of Collateral Used
Collateral is the security used against loans which must be within the ability of the target
clientele. Yet it must be sufficient to deter default or in the event of default, to ensure that the
institution recovers the advanced loan. The use of savings and group-guarantee are some of the
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types of security which have proved successful. Table 95 below shows that guarantors were the
predominant collateral in both DCA 1 and DCA 3 followed by household goods.
Table 95: Type of collateral used in DCA 1 and DCA 3
 Type of collateral       DCA Area        Total
 used                  DCA1     DCA3
 None                      7        2         9
 Milk deliveries            4        3        7
 Title deed                 6        4       10
 Guarantor(s)              15        26      41
 Household goods            9        15      24
 Savings/Shares             2        24      26
 Others (specify)           1        0        1
 Log book                   0        2        2
 Cattle                     6        14      20
 Total                    50        90     140
Source: Baseline Survey, April 2009

This baseline survey found that 29% of the loans were secured with guarantors from a solidarity
group as the collateral and that only 5% were secured with milk deliveries as the collateral as
shown in Figure 39 below. This reflects the predominance of MFIs, cooperatives and ROSCAS
as the main sources of credit in the project area. These findings are also significant because they
suggest only 7% of the loans are secured against title deeds which is collateral of choice for large
and long term development loans. This means that very few farmers have access to these vital
documents or are willing to use them to access credit because of past experiences where farmers
have lost their land in the event of defaulting.
Figure 38: Type of Collateral




Source: Baseline Survey, April, 2009

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4.50.8 Amount Paid at Maturity
The amount that was repaid at maturity for the loans that were disbursed ranged from Kshs 5,150
to Kshs 575,000 both of which were disbursed in Lugari District. These two extremes show the
diversity of the land holdings in Lugari District which has a mix of large scale farms and small
holdings. Table 96 below shows that the average loan repayment at maturity in DCA 1 was Kshs
73,026 and Kshs 68,372 in DCA 3 respectively. However, the spread of the loan repayment
shown by the large standard deviation reflects the wide income disparity in the communities
where a few farmers are able to secure large loans while the majority can only secure small loans.
Table 96: Amount paid at maturity Kshs
 DCA Area         N         Minimum      Maximum     Mean       Std. Deviation
 DCA1                 50        5,150     575,000   73,026.34      96,853.473
 DCA3                 90        5,500     392,000   68,372.32      74,680.922
 Total               140         5,150    575,000   70,034.47      82,963.100
Source: Baseline Survey, April 2009


The average loan repayment in the project area was Kshs 70,034. Within the project area, Nakuru
district paid the highest amount at maturity averaging Kshs 130,187 while Nandi North District
had the lowest amount disbursed which averaged Kshs 26,360 as shown in Table 97 below.
Table 97: Amount paid at maturity Kshs
                                                             Std.
 District         Minimum      Maximum     Mean           Deviation
 Bomet                5,500      127,800  42,746.15        39,983.176
 Kisii Central        5,900      160,615  46,125.83        58,747.294
 Nyamira            12,000        47,000 27,096.47          8,342.172
 Nandi North          6,000       46,000 26,360.00         15,506.386
 Trans Nzoia        24,000       310,000  98,735.71        77,375.663
 Bungoma            17,220       330,000  73,419.47        78,345.807
 Lugari               5,150      575,000 102,477.00       132,873.854
 Uasin Gishu        11,500       180,000  52,546.34        40,239.417
 Nakuru             22,200       213,000 130,087.50        74,282.308
 Total                5,150      575,000  70,034.47        82,963.100
Source: Baseline Survey, April 2009

4.51 Natural Resource Management Problems
To establish the impact of individual households on the natural resource management,
respondents were asked to indicate the problems that they face in managing natural resources.
Table 91 below indicates that soil erosion was by far the most common problem facing 14% of all
the households in the project area followed by deforestation and water pollution. Given that soil
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erosion and water pollution are consequences of deforestation, these findings suggest that SDCP
should incorporate messages afforestation and soil conservation messages during training.
Table 98: Natural Resource Management Problems by Ditrict
 Natural resource
 management                   Kisii             Nandi     Trans                        Uasin
 problem            Bomet Central Nyamira North           Nzoia    Bungoma   Lugari    Gishu    Nakuru     Total
 Soil erosion           11          7       12      12        10        14       10        27        9      112
 Deforestation          11          7       11      10        10        10       10        15       10       94
 Water pollution        12          6       11      13        11         6         9       10       11       89
 Market places          12          6       11      11        12         6       11         6       11       86
 Soak pits              11          7       11        8       11         8       11         2       11       80
 Cattle dips            11          7       11        9       11         8       11         4       11       84
 Manure disposal         9          6       11        4       11         8       11         4       11       80
 Sand harvesting         9          7       11        5       10         8         9       10       11       80
 Human/wildlife
                         9          7       12        5       10        10        9        16          9        87
 conflict
 Air pollution           0          0        0        0        0         0        0         1         0       1
 Ants                    0          0        0        0        0         0        0         2         0       2
 Total                  95        60      101       83        96        78       91        97        94     795
Source: Baseline Survey, April 2009.

Figure 40 below shows the relative importance of the problems associated with the natural
resource management in the project area.
Figure 39: Problems associated with Natural Resource Management




Source: Baseline Survey, April 2009




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4.52 Use of waste from dairy enterprise
The survey found that more than 98% of all the farmers in the project area used waste from the
dairy enterprise as a fertilizer and that only 0.3% were it using to produce biogas as shown in
Figure 41 below.

Figure 40: Use of waste from Dairy Enterprise




Source: Baseline Survey, April 2009


4.53 Severity of the NRM problems
To assess the severity of the NRM problems in the project area, respondents were asked their
opinion on the severity of the problem whose results are shown in Table 99 below.

Table 99: Severity of NRM across the project area
                                                           District                                              Total
 Severity of             Kisii                  Nandi      Trans                         Uasin
 problem       Bomet    Central    Nyamira      North      Nzoia    Bungoma   Lugari     Gishu       Nakuru
 Low              78        60         78            71        86        70      82           40         79       644
 Moderate         13           0       12            11          8        5        8          42          6       105
 Serious           3           0         9            1          2        1        0           8          8        33
 Critical          1           0         2            0          0        0        1           7          1        12
 Total            95        60        101            85        96        78      91           97         94       795
Source: Baseline Survey, April 2009

Figure 42 below summarizes this information and shows that 81% of the households in the
project area did not feel they had a problem and only 2% considered the problem to be critical.




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Figure 41: Severity of NRM Problems




Source: Baseline Survey, April 2009


4.54 Household Assets
The household assets were measured by looking at the materials that were used to construct the
homes of the respondents in the project area.

4.54.1 Roof Materials
This study showed that 94% of the houses in the project area had corrugated iron roofs, 4.5% had
thatched roofs, 1% had tin roofs and 0.6% had tiles. Bomet District had the largest number of
households with thatched roofs while Lugari and Nakuru had the largest number with tiled roofs
as shown in Table 100 below.
Table 100: Roof material used to construct residence of household head
                     Roof material used to construct residence of
                                   household head                       Total
                                            Corrugated
 District         Thatch        Tin            iron           Tiles
 Bomet                  14            0                81           0           95
 Kisii Central            1           0              59            0            60
 Nyamira                  5           1              95            0        101
 Nandi North              3           0              80            0            83
 Trans Nzoia              1           0              95            0            96
 Bungoma                  0           0              78            0            78
 Lugari                   4           1              84            2            91
 Uasin Gishu              8           6              82            1            97
 Nakuru                   0           0              92            2            94
 Total                  36            8             746            5        795
Source: Baseline Survey, April 2009

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4.54.2 Wall Materials
This study showed that 53% of the households in the project areas used mud as the wall
materials in constructing their houses as shown in Table 101 below. Farmers in DCA 1 in Bomet
District had the highest proportion of mud houses. This suggests that majority of the households
targeted by the project are low income households.
Table 101: Wall Materials by District and DCA
 District         Material            DCA1    DCA3   Total
 Bomet            Mud                    96%     25%     62%
                  Straw                    0%     3%       1%
                  Brick                    2%     5%       4%
                  Concrete                 0%     8%       4%
                  Concrete/mud             0%     8%       4%
                  Wood                     2%    53%     26%
                  Total                 100%    100%   100%
 Kisii Central    Mud                    47%     49%     48%
                  Brick                  29%     36%     33%
                  Concrete               24%     10%     15%
                  Concrete/mud             0%     5%       3%
                  Total                 100%    100%   100%
 Nyamira          Mud                    39%     46%     44%
                  Brick                  32%     22%     26%
                  Concrete               29%     32%     31%
                  Total                 100%    100%   100%
 Nandi North      Mud                    66%     73%     70%
                  Brick                    9%     5%       7%
                  Concrete               13%     11%     12%
                  Concrete/mud           13%     11%     12%
                  Total                 100%    100%   100%
 Trans Nzoia      Mud                    66%     51%     59%
                  Straw                    0%     2%       1%
                  Brick                    4%    37%     20%
                  Concrete                 2%     2%       2%
                  Concrete/mud           28%      7%     18%
                  Total                 100%    100%   100%
 Bungoma          Mud                    33%     63%     51%
                  Brick                  26%     28%     27%
                  Concrete               15%      9%     11%
                  Concrete/mud           26%      0%     10%

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 District         Material            DCA1   DCA3   Total
                  Total                 100%   100%   100%
 Lugari           Mud                    47%    61%     58%
                  Straw                   0%     1%       1%
                  Brick                  11%    15%     14%
                  Concrete               21%    15%     16%
                  Concrete/mud           21%     7%     10%
                  Total                 100%   100%   100%
 Uasin Gishu      Mud                    75%    68%     71%
                  Straw                   3%     4%       3%
                  Brick                   8%    12%     10%
                  Concrete                3%     4%       3%
                  Concrete/mud           10%    12%     11%
                  Wood                    3%     0%       1%
                  Total                 100%   100%   100%
 Nakuru           Mud                    21%    30%     26%
                  Brick                   8%     0%       3%
                  Concrete               56%    54%     55%
                  Concrete/mud            3%     6%       4%
                  Wood                    5%     9%       8%
                  Corrugated
                  iron                     8%         2%      4%
                  Total                  100%       100%    100%
Source: Baseline April 2009

Figure 42: Wall Materials used in Constructing Households




Source: Baseline Survey, April 2009



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4.54.3 Floor Materials
The survey showed that 54% of the households had earth floors while 43.8% had concrete floors.
It is only 2.1% of the households that had wood floors. The distribution of these households is
shown in Table 102 below.
Table 102: Floor material used to construct residence of household head
 District                     DCA Area              Total      Source: Baseline Survey, April 2009
                  Material DCA1          DCA3
 Bomet           Earth             98%        58%       79% 4.54.4 Window materials in use
                 Concrete           2%        40%       20%
                                                               The study showed that 48% of the window
                 Wood               0%         3%         1%
                                                               materials used was glass and 48% was
                 Total           100%        100%      100%
                                                               wood, 2.4% was wire mesh and 0.6% was
 Kisii Central   Earth             44%        49%       47%
                                                               open most of which were in Uasin Gishu
                 Concrete          53%        49%       51%
                 Wood               3%         2%         2% District as shown in Table 103 below.
                 Total           100%        100%      100%
                                                               Table 103: Window material used to
 Nyamira         Earth             39%        46%       44% construct residence of household head
                 Concrete          61%        48%       52%
                 Wood               0%         6%         4%
                 Total           100%        100%      100%
 Nandi North     Earth             63%        77%       71%
                 Concrete          34%        23%       28%
                 Wood               3%         0%         1%
                 Total           100%        100%      100%
 Trans Nzoia     Earth             89%        42%       67%
                 Concrete          11%        51%       30%
                 Wood               0%         7%         3%
                 Total           100%        100%      100%
 Bungoma         Earth             48%        67%       60%
                 Concrete          52%        33%       40%
                 Total           100%        100%      100%
 Lugari          Earth             47%        54%       52%
                 Concrete          47%        42%       43%
                 Wood               5%         4%         5%
                 Total           100%        100%      100%
 Uasin Gishu     Earth             55%        60%       58%
                 Concrete          43%        40%       41%
                 Wood               3%         0%         1%
                 Total           100%        100%      100%
                 Earth             23%        33%       29%
 Nakuru
                 Concrete          74%        67%       70%
                 Wood               3%         0%         1%
                 Total           100%        100%      100%


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                    Window material used to construct residence of household head         Total

 District           Wire mesh            Tin       Wood          Glass         Open
 Bomet                       0                 0          73             22           0           95
 Kisii Central                 0               0          35             25           0           60
 Nyamira                       0               0          37             64           0        101
 Nandi North                   0               0          45             38           0           83
 Trans Nzoia                   3               0          62             30           1           96
 Bungoma                       2               1          34             41           0           78
 Lugari                        3               3          40             45           0           91
 Uasin Gishu                   1               2          38             52           4           97
 Nakuru                     10                 0          20             64           0           94
 Total                    19                   6      384            381              5        795
Source: Baseline Survey, April 2009



4.55 Support to Policy and Institutions
The survey also included interviews with CAIS, DTI and KDB. Generally, support to policy and
legislative      development       for   the animal   feeds      sub-sector,    development       of   a   strategy for
commercialization/privatization of Central Artificial Insemination Station (CAIS), harmonization of breed
services including recording and AI services and a stakeholder validation process was on track. The
support to KDB to set up and operation of a DIC, linked to the Low-Cost Market Information System
(LCMIS) was also on track.


However, curricular and technical strengthening of the Dairy Training Institute (DTI) was behind schedule
because financing arrangement was such that DTI needed to have funds upfront to spend and then claim
reimbursements against those expenses. Given that the institute has a very weak cash flow, unlike CAIS
and KDB which have independent funds, the situation will not improve until an alternative financing
mechanism is developed.




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5     CONCLUSIONS AND RECOMMENDATIONS


The findings of this survey show positive change in some parameters between DCA 1 and DCA
3. These changes could arise from two sources namely: the investment that SDCP has made in
DCA 1 and that some areas in DCA 3 have had previous dairy investments from other projects.
For instance, whereas the average land size in DCA 1 is 4.72 acres, the average land holding in
DCA 3 is 4.47 acres. This is consistent with the project goal and objectives because it shows that
SDCP is improving its targeting strategy towards smallholder farmers as it moves from DCA 1 to
DCA 3. However, SDCP needs to continue refining its targeting strategy to ensure that the project
doesn’t leave out needy groups because this survey shows that there are small pockets of non-
poor dairy households in each DCA.


The groups that SDCP is currently working with are very diverse and at different levels of development.
This means that SDCP has to develop customized training processes to meet these diverse needs.


5.1     Sustainability
To improve sustainability of the project interventions, a number of recommendations emerged from this
survey:
      1. SDCP should improve targeting of individuals being trained. The targeting will be at two levels.
          First, SDCP should ensure that individuals who manage dairy animals are trained and not
          community gate keepers. This requires taking time to understand the role that vocal and influential
          individuals play in the community. The strategy should then be to turn the gate keepers into allies
          by treating them with respect, humor and compassion. Secondly, SDCP should improve the
          organization of the training to attract more women participants by looking at the timing of the
          training and distance to be covered.

      2. Encourage community in-kind and cash contributions. While SDCP was providing a token
          allowance to community participants to meet the transport and lunch expenses, experiences from
          other community projects show that to enhance sustainability, SDCP should encourage
          participants to make in-kind and cash contributions to meet some of the training expenses. This
          entrenches the market system which is central to commercialization.



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    3. Build the capacity of self selected community service providers. In each community, there are
        individuals with uncommon skills or competence in dairy enterprise willing to share their
        knowledge with other farmers. The challenge of SDCP is to identify these self selected service
        providers in each community and build their capacity to complement the role of the extension
        workers. The key advantage with these individuals is that they teach by example and therefore
        credible.

    4. Support farmer to farmer extension services. To maximize the impact of the project resources
        SDCP should put in place a mechanism for screening farmers to ensure that study tours only
        benefit farmers that are willing to learn and share with their peers. Besides improving the
        technical skills in dairy production, SDCP should facilitate farmers to acquire other skills needed
        to undertake farming as a business. This will help farmers to see the connection between
        profitability of dairy enterprise and skills they need to sustain the business.

    5. To complement peer training within the community, SDCP should promote match making
        between farmers in the same neighborhood with others outside the project area who offer
        important lessons to learn. The groups that qualify for this role should be identified in consultation
        with other well informed individuals outside the project area such as processors and managers of
        dairy projects. Some of the groups that could qualify for match making include outstanding
        farmers and cooperatives that have overcome similar challenges to create commercially viable
        dairy businesses that have improved the livelihoods of their families, communities and other
        stakeholders in the business.

    6. Livestock production is one of the major causes of the world's most pressing environmental
        problems, including global warming, land degradation, air and water pollution, and loss of
        biodiversity. However, livestock have a large potential to solve environmental problems and make
        major improvements at reasonable cost. SDCP should therefore support interventions that mitigate
        the negative impact of livestock on climate change such as agro-forestry, water harvesting and
        zero-grazing interventions.

This survey showed that farmers in DCA 1 spent about Kshs 556 in providing supplementary feeds to their
dairy herd compared to the farmers in DCA 3 who incurred only Kshs 179 shilling in supplementary
feeds. This is an indication of the realization of need to improve milk production. However, the daily
average milk production in DCA 1 was 8.83 litres compared to 9.81 litres per day in DCA 3. The lower
production suggests other constraints such as disease burden may be limiting milk production.



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Using expenditure as a proxy for income, this survey suggests that DCA 1 has a lower income than DCA
3. For instance, the average monthly expenditure in DCA 1 was Kshs 20,847 compared to Kshs 23,642 in
DCA 3. SDCP is also targeting relatively poor communities based on the nutritional and household
welfare indicators. Given that the average monthly expenditure of dairy producing households in the
project area is Kshs 21,423, the project will continue facing the challenge of getting poor households into
dairy because the high cost of dairy cows is a significant barrier to entry in dairy farming. For instance,
farmers in DCA 1 paid an average of Kshs 26,532 to for a dairy cow while farmers in DCA 3 were paying
an average of Kshs 26,643.


This study suggests that investment of the Smallholder Dairy Commercialization Project in DCA 1 has
also resulted in stability of employment opportunities. For instance, the average dairy household in DCA 1
had an average of 1.24 permanent employees compared to 1.15 permanent employees in DCA 3.
However, DCA 1 had only 1.25 casual employees compared to 1.37 casuals in DCA 3. This suggests that
the farmers in DCA 3 are substituting permanent employees with casual workers.


The study also found that dairy cows in DCA 1 required an average of 1.2 inseminations before
conception compared to 1.44 inseminations in DCA 3. This suggests that dairy cows in DCA 1 had a
slightly higher breeding efficiency compared to those in DCA 3 which is an indication of better
knowledge in timely heat detection and improved service delivery. This conclusion is further confirmed by
the fact that the calving interval in DCA 1 was about 15.9 months compared to 16.2 months in DCA 3.


This survey also found that investment by SDCP had reduced the cost AI services in DCA 1 to an average
of Kshs 780.3 compared to the cost of AI service in DCA which was Kshs 828.9 per service. The similar
cost reduction also found in the delivery of animal health services where the average cost was Kshs 416.80
in DCA 1 compared to Kshs 427.90 in DCA 3.


Despite these gains, there were performance indicators where the investment in DCA 1 appears to have
registered mixed results. For instance, only 30.7% of the farmers in DCA 1 were keeping records regularly
compared to 41.6% of the farmers in DCA 3 despite nearly two years of training farmers on record
keeping. This suggests that there is need to refine the methods used to train farmers and simplify the
extension messages to increase adoption. In addition, only 40.5% of the farmers in DCA 1 preferred using
AI services over the bull service compared to 45.8% of the farmers in DCA 3. The high preference for bull
service is driven by a combination of high costs and poor reliability of the AI service providers in many


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parts of the project area. SDCP needs to intensify efforts to train farmers in heat detection and monitoring
service delivery so as to increase the confidence of farmers to AI services.


This study found that the average farmer in DCA 1 produced 8.84 litres of milk per day compared to
farmers in DCA 3 who produced 9.81 litres per day. The study also showed that farmers in both DCA 1
and DCA 3 sold about the same amount of milk which was about 6.04 litres per day. This study therefore
suggests that the extra milk produced above this threshold in DCA 3 is currently retained for home
consumption
The average daily revenue from milk sales in the project area is Kshs 154 from the sale of 6.2 litres at
average price of Kshs 24.8 per litre. While this provides an income of nearly US$ 2/day, it is still largely
financed by unpaid family labour but in turn the enterprise contributes to family welfare and nutrition
from 3.1 litres of the milk retained on the farm daily.


There is a huge unmet need for information and knowledge on basic animal husbandry and management
especially in feeding. However, the costs of producing fodder appear to outweigh other constraints as the
reason for not feed supplements for the majority of the farmers. SDCP needs to continuously seek
technologies that can reduce the cost of producing fodder if this is to be useful to most farmers.


This study showed that 33% of the households were making regular savings in DCA 1 compared to 36%
in DCA 3. This is not statistically significant and we conclude that savings behaviour was the same across
the DCAs. Accessing credit is still a major challenge in the project area and the survey showed that only
18.5% of the households were able to access credit. However, demand for credit is still highly skewed
towards consumption rather than investment. This means that SDCP needs to build partnerships with other
institutions that can develop suitable financial products to meet the needs of the poor dairy producing
households especially the ones without title deeds or those intending to enter into dairy enterprise.


Construction of infrastructure in the Dairy Training Institute (DTI) has been delayed. This is arising from
two factors. First, the SDCP mode of operation is such that the institute is expected to spend from its
reserves and then request for reimbursements against those expenses. Given DTI’s tight cash-flow
situation, implementation of this component will not be on track unless this requirement is relaxed or DTI
receives other funding.




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5.2     Recommendations
The survey identified nine key interventions that SDCP needs to put in place the following interventions in
DCA 3:

      1. Development is about transforming communities and their institutions. Based on this
          understanding, SDCP should build the capacity of the dairy cooperatives and farmer organizations
          through training in order to enhance: a) efficiency and effectiveness;           b) sustainability of
          cooperatives – both short and long run, c) building confidence, trust and respect for sustainable
          shared goals; d) adaptability to changing environment; e) interaction with external agents; f)
          diversification of activities to maximize institutional and individual interest and g) expansion and
          replication of cooperatives. This baseline survey recommends SDCP should strengthen group
          organization and development through capacity building activities in DCA 3 to bring about
          sustainable community and institutional transformation.

      2. Provide technical support and strengthen linkages between farmers, credit agencies and other
          organizations both private and public promoting dairy to facilitate technology transfer

      3. Besides improving the technical skills in dairy production, SDCP should facilitate farmers to
          acquire other skills needed to undertake farming as a business. In particular, SDCP efforts should
          focus on training farmers to managing production costs need to sustain the business. Hence this
          study recommends that SDCP should enhance dairy enterprise development and business.

      4. Strengthen market linkages across the dairy value chain.

      5. One of the main challenges in developing the dairy enterprise in the project area is the fact that it
          is a patriarchal society in which there is resistance for women to play a greater role commensurate
          with their contribution to the dairy enterprise. SDCP should therefore work closely with other
          organizations pursuing gender mainstreaming in the programme area to educate the community on
          the need to encourage women to play a larger role in all aspects of the dairy enterprises.

      6. While this baseline survey has collected a lot information on the project area, however, there are
          some outstanding issues that once resolved would improve and refine targeting of interventions in
          DCA 3. Subsequently, this survey recommends that SDCP should carry out an in-depth study of
          milk marketing to determine how the costs and benefits of the dairy enterprise are shared by
          between various stakeholders across the dairy value chain.

      7. To maximize impact of the dairy interventions, SDCP should carry out a training needs
          assessment to prioritize the training needs of different groups in the transformation continuum.
          This only gives an indication of where to start.
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8. SDCP should carry out an in-depth study to assess the impact of HIV/AIDS, environment, gender
    and the youth on the dairy enterprise.

9. SDCP should mainstream gender analysis and the selection of both men and women farmers

10. SDCP should look for affordable mechanisms and work with appropriate institutions to facilitate
    livestock registration.

11. To reduce the incidence of tick borne diseases from 23% SDCP should focus extension messages
    to enhance adoption of all sustainable tick control practices.

12. To increase adoption in the use of AI services, SDCP should strengthen its linkages with other
    public and private agencies that are promoting similar goals.




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