final sdcp baseline report- june 30, 2009

8/13/2019 Final SDCP Baseline Report- June 30, 2009

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SDCP Baseline Survey Report

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MINISTRY OF LIVESTOCKDEVELOPMENT

SMALLHOLDER DAIRY COMMERCIALIZATION PROGRAMMECONTRACT NO: CONS/SDCP/1/2007- 2008

BASELINE SURVEY REPORT

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

JUNE 2009

FIBEC Limited Bomas of Kenya, Off Forest Edge Road LangataP.O. Box 1031600100 GPO NairobiTel: 254-020-892117

Cell: +254 733 223 558 or +254 722 310239Fax: 254-020-891892Email: [email protected]




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Table of Contents

1 EXECUTIVE SUMMARY .................................................................................................................................. IX

2 INTRODUCTION...............................................................................................................................................1

2.1 PROGRAMMEGOAL .....................................................................................................................................1 2.2 PROGRAMMEPURPOSE .................................................................................................................................1 2.3 PROGRAMMECOMPONENTS ..........................................................................................................................1 2.4 SCOPE OF THEASSIGNMENT ...........................................................................................................................2

3 METHODOLOGY ..............................................................................................................................................3

3.1 O VERVIEW OF THE M ETHODOLOGY ..................................................................................................................3 3.2 A REA OF C OVERAGE .....................................................................................................................................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 T RAINING OF E NUMERATORS ..........................................................................................................................7 3.5 SOURCES OF D ATA AND C OLLECTION T ECHNIQUES ................................................................................................7 3.6 SECONDARY D ATA SOURCES ...........................................................................................................................7 3.7 LITERATURE REVIEW .....................................................................................................................................8 3.8 K EY INFORMANTS .........................................................................................................................................8 3.9 F OCUS G ROUP D ISCUSSIONS AND K EY INFORMANT INTERVIEWS ..............................................................................9 3.10 F IELD V ISITS ............................................................................................................................................. 10 3.11 C ASE STUDIES ........................................................................................................................................... 10 3.12 P HOTOGRAPHS .......................................................................................................................................... 10

4 STUDY FINDINGS ........................................................................................................................................... 11

4.1 NUTRITIONALSTATUS ................................................................................................................................. 11 4.2 HOUSEHOLDS............................................................................................................................................ 14 4.3 LEVEL OFEDUCATION .................................................................................................................................. 14 4.4 HOUSEHOLDSIZE ....................................................................................................................................... 15 4.5 MAINOCCUPATION OFHOUSEHOLDHEAD ...................................................................................................... 16 4.6 LANDSIZE ................................................................................................................................................ 18 4.7 LANDOWNERSHIP ..................................................................................................................................... 19 4.8 LANDUSE ................................................................................................................................................ 21 4.9 MILKINGHERD .......................................................................................................................................... 23 4.10 MILKPRODUCTION .................................................................................................................................... 24

4.11 FARM RECORDS ......................................................................................................................................... 26 4.12 HOUSEHOLDWELFARE ................................................................................................................................ 28 4.13 MAINFEEDS ............................................................................................................................................. 35 4.14 SUPPLEMENTARYFEEDS ............................................................................................................................... 37 4.15 COST OFSUPPLEMENTARYFEEDS ................................................................................................................... 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 OFMILKPRODUCTION ......................................................................................................................... 45 4.19 WATERSOURCES ....................................................................................................................................... 47 4.20 ADEQUACY OFWATER ................................................................................................................................ 49 4.21 CHOICE OFANIMALBREEDS.......................................................................................................................... 50 4.22 PREFERREDBREEDINGMETHODS ................................................................................................................... 51 4.23 CHOICE OF THEPREFERREDBREEDINGMETHODS ............................................................................................... 52 4.24 BREEDINGRELATEDCOSTS ........................................................................................................................... 53 4.25 BREEDINGEFFICIENCY ................................................................................................................................. 56 4.26 CALVINGINTERVAL ..................................................................................................................................... 57 4.27 MILKPRODUCTION, SALES ANDCONSUMPTION................................................................................................. 58

4.27.1 Milk Bars and other milk outlets ................................................................................................... 60 4.28 MILK HANDLING PRACTICES .......................................................................................................................... 61 4.29 MILKMARKETINGCONSTRAINTS ................................................................................................................... 63 4.30 MILKPROCESSING...................................................................................................................................... 65 4.31 SKILLSREQUIRED TOIMPROVEPROFITS INDAIRYFARMING .................................................................................. 66 4.32 TYPES ANDORGANIZATION OFCOMMUNITYGROUPS ......................................................................................... 68

4.33 SIZE OF THEGROUPS................................................................................................................................... 73 4.34 REGISTEREDCOWS ..................................................................................................................................... 73 4.35 ANIMALHEALTHMANAGEMENT ANDDELIVERY ................................................................................................ 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 EMPLOYMENTCREATION INDAIRYENTERPRISES ................................................................................................ 76 4.37 BREEDDISTRIBUTION .................................................................................................................................. 77 4.38 HERDSTRUCTURE ...................................................................................................................................... 79 4.39 COST OFBUYINGDAIRYANIMALS .................................................................................................................. 82 4.40 PRODUCTIONSYSTEM ................................................................................................................................. 83 4.41 COST OFZERO GRAZING .............................................................................................................................. 85

4.42 FARM INFRASTRUCTURE .............................................................................................................................. 85 4.43 COST OFLABOUR ....................................................................................................................................... 86 4.44 CONDITION OF MILKING SHED ..................................................................................................................... 87 4.45 GENDER INDAIRY ...................................................................................................................................... 88 4.46 GENDERDIVISION OFLABOUR ....................................................................................................................... 90 4.47 SAVINGS ANDCREDIT .................................................................................................................................. 94 4.48 LOANAPPLICATIONS ................................................................................................................................... 98 4.49 TYPE OFLENDER ...................................................................................................................................... 100 4.50 LOANPRODUCTS ..................................................................................................................................... 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 NATURALRESOURCEMANAGEMENTPROBLEMS .............................................................................................. 110




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4.52 USE OF WASTE FROM DAIRY ENTERPRISE ........................................................................................................ 112 4.53 SEVERITY OF THENRM PROBLEMS ............................................................................................................... 112 4.54 HOUSEHOLDASSETS ................................................................................................................................. 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 ANDINSTITUTIONS ......................................................................................................... 117

5 CONCLUSIONS AND RECOMMENDATIONS .................................................................................................. 118

5.1 SUSTAINABILITY ....................................................................................................................................... 118 5.2 RECOMMENDATIONS ................................................................................................................................ 122




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List of Figures

FIGURE1: MAP SHOWING THEAREA COVERED BY THESURVEY ................................................................................................5 FIGURE2: EDUCATIONLEVEL OFDAIRYFARMERS ............................................................................................................... 14 FIGURE3: MAINOCCUPATION OFHOUSEHOLDHEAD INDCA 1 ............................................................................................ 17 FIGURE4: MAINOCCUPATION OFHOUSEHOLDHEADS INDCA 3 ........................................................................................... 17 FIGURE5: LAND USE ................................................................................................................................................... 22 FIGURE6: AVERAGE HERDSIZE BYDISTRICT IN THE PROJECT AREA........................................................................................... 23 FIGURE7: DISTRIBUTION OF MILK PRODUCTION ACROSS THESDCP AREA ................................................................................. 26 FIGURE8: MEANMONTHLYHOUSEHOLDEXPENDITURE ...................................................................................................... 29 FIGURE9: MAP SHOWING THEMEANHOUSEHOLDEXPENDITURE .......................................................................................... 34 FIGURE10: MAINANIMAL FEEDS IN THEPROJECTAREA ...................................................................................................... 36 FIGURE11: MAIN ANIMAL FEEDS BYDISTRICT ................................................................................................................... 37 FIGURE12: AVERAGEDAILYCOST OFSUPPLEMENTARYFEEDS INDRYSEASON .......................................................................... 39 FIGURE13: AVERAGECOSTS OFMILKPRODUCTION(WETSEASON) ....................................................................................... 40 FIGURE14: FEEDCONTINGENCYMEASURES INDCA 1 ........................................................................................................ 43 FIGURE15: FEEDCONTINGENCYMEASURES INDCA 3 ........................................................................................................ 43 FIGURE16: COST OFMILKPRODUCTION DURING THEDRYSEASON ........................................................................................ 46 FIGURE17: COST OFMILKPRODUCTION DURING THE WET SEASON ........................................................................................ 46 FIGURE18: MAINSOURCES OFWATER DURING THEWETSEASON ......................................................................................... 47 FIGURE19: PREFERENCE FORBULLSERVICE BYDISTRICT INDCA1 AND DCA 3 ......................................................................... 52 FIGURE20: AVERAGEDAIRYREVENUE FROMMILKSALES INKSHS.......................................................................................... 60 FIGURE21: MILK HANDLING PRACTICES ........................................................................................................................... 61 FIGURE22: ON-FARM MILKPROCESSING ......................................................................................................................... 66 FIGURE23: SKILLSNEEDED TOINCREASEPROFITABILITY OFDAIRYENTERPRISE .......................................................................... 67 FIGURE24: DISTRIBUTION OFDAIRYCATTLEBREEDS IN THEPROJECTAREA ............................................................................. 78

FIGURE25: MAP SHOWING THEBREEDDISTRIBUTION IN THEPROJECTAREA ............................................................................ 79 FIGURE26: GENDER OF THE HOUSEHOLD HEAD .................................................................................................................. 89 FIGURE27: GENDER OF THEHOUSEHOLDHEADS BYDISTRICT ............................................................................................... 90 FIGURE28: DISTRIBUTION OF THEHOUSEHOLDS MAKINGSAVINGS ........................................................................................ 95 FIGURE29: PREFERREDMODE OFSAVING ........................................................................................................................ 96 FIGURE30: PREFERREDMETHODS OFSAVINGS ................................................................................................................. 96 FIGURE31: LOAN APPLICATION BYMONTH ...................................................................................................................... 99 FIGURE32: REASONS WHY FARMERS BORROWED THE PREVIOUS SEASON ................................................................................ 100 FIGURE33: TYPE OFLENDER ....................................................................................................................................... 101 FIGURE34: LOAN SUCCESSRATE ................................................................................................................................. 103

FIGURE35: REASONS FOR UNSUCCESSFUL LOAN APPLICATIONS ............................................................................................ 104 FIGURE36: TYPE OFPAYMENT .................................................................................................................................... 105 FIGURE37: MEANLOAN SIZE ANDINTERESTRATES .......................................................................................................... 106 FIGURE38: TYPE OFCOLLATERAL ................................................................................................................................. 109 FIGURE39: PROBLEMS ASSOCIATED WITHNATURALRESOURCEMANAGEMENT ....................................................................... 111 FIGURE40: USE OF WASTE FROMDAIRYENTERPRISE ........................................................................................................ 112 FIGURE41: SEVERITY OFNRM PROBLEMS ..................................................................................................................... 113




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FIGURE42: WALLMATERIALS USED INCONSTRUCTINGHOUSEHOLDS ................................................................................... 115

List of Tables

TABLE1: A DMINISTRATIVE A REAS OF DCA S IN THE P ROGRAMME A REA .................................................................................4 T ABLE 2: NUMBER OF HOUSEHOLDS INTERVIEWED BY DISTRICT AND DCA .............................................................5 TABLE3: NUTRITIONSTATUS OFCHILDREN AMONG THEPOOR ANDNON-POOR HOUSEHOLDS IN THEPROJECTAREA .......... .............. . 11 TABLE4: HIGHESTEDUCATION LEVEL OF HOUSEHOLD HEADS BYDISTRICT ANDDCA ................................................................... 15 TABLE5: S IZE OF HOUSEHOLD BY DISTRICT AND DCA ............................................................................................. 16 TABLE6: MAINOCCUPATION OF THEHOUSEHOLDHEAD BYDISTRICT INDCA 1 ........................................................................ 18 TABLE7: MAINOCCUPATION OF THEHOUSEHOLDHEAD BYDISTRICT INDCA 3 ........................................................................ 18 TABLE8: HOW MUCH LAND IS AVAILABLE TO THIS FAMILY ? ....................................................................................... 19 TABLE9: LANDOWNERSHIP BYDISTRICT INDCA 1............................................................................................................. 20 TABLE10: LANDOWNERSHIP BYDISTRICT INDCA 3 ........................................................................................................... 20 TABLE11: CIRCUMSTANCES OF DAIRY FARMERS WHO DID NOT OWN LAND ................................................................................ 21 TABLE12: LAND USE INDCA1 BYDISTRICT ...................................................................................................................... 22 TABLE13: LAND USE INDCA 3 BYDISTRICT ...................................................................................................................... 23 TABLE14: AVERAGE SIZE OF THE MILKING HERD BY BREED BYDISTRICT INDCA 1 ....................................................................... 24 TABLE15: AVERAGE SIZE OF THE MILKING HERD BY BREED BYDISTRICT INDCA 3 ....................................................................... 24 TABLE16: AVERAGE MILK PRODUCTION OF THE DAIRY HERD IN LITRES/ DAY BYDISTRICT .............................................................. 25 TABLE17: PROPORTION OF HOUSEHOLDS KEEPINGFARM RECORDS INDCA1 AND DCA 3 ........................................................... 27 TABLE18: TYPE OF FARM RECORDS KEPT BY FARMERS INDCA 1 AND DCA 3 BYDISTRICT ............................................................ 28 TABLE19: HOUSEHOLDMONTHLYEXPENDITURE BYTYPE, OCCUPATION ANDDISTRICTS INDCA 1 ................................................ 30 TABLE20: HOUSEHOLDEXPENDITURE BY SOURCE OFINCOME INDCA 3 BYDISTRICT AND BYTYPE ............. ............. ............. .......... 32 T ABLE 21: COST OF WATER IN KSHS PER DAY BETWEEN DCA 1 AND DCA 3 ......................................................... 35 TABLE22: COST OF WATER IN KSHS PER DAY .......................................................................................................... 35 T ABLE 23: M AIN LIVESTOCK FEED IN DCA 1 AND DCA 3 ....................................................................................... 36

TABLE24: AVERAGE QUANTITY OF SUPPLEMENTARY FEEDS USED DURING THE WET SEASON INDCA 1 AND DCA 3 .......... .............. ..... 37 TABLE25: AVERAGE COST OF SUPPLEMENTARY FEEDS IN KSHS DURING THE WET SEASON IN DCA 1 AND DCA 3 ... 38 TABLE26: AVERAGE COST OF FEED SUPPLEMENTS DURING THE WET SEASON ......................................................... 38 TABLE27: REASONS WHY FARMERS DON’T USE SUPPLEMENTS INDCA 1 BYDISTRICT ................................................................. 41 TABLE28: REASONS WHY FARMERS DON’T USE SUPPLEMENTS INDCA 3 BYDISTRICT .................................................................. 42 TABLE29: FEED CONTINGENCY MEASURES IN DCA 1 .............................................................................................. 44 TABLE30: FEED CONTINGENCY MEASURES IN DCA 3 .............................................................................................. 45 TABLE31: M AIN WATER SOURCES IN DCA 1 AND DCA 3 DURING WET SEASON ....................................................... 48 TABLE32: MAIN WATER SOURCES INDCA 1 AND DCA 3 DURING DRY SEASON .......................................................................... 48 TABLE33: MAIN SOURCE OF WATER DURING THE WET SEASON BYDISTRICT .............................................................................. 49 TABLE34: STATUS OF WATER ADEQUACY THROUGHOUT THE YEAR IN DCA 1 AND DCA 3 ..................... ............. ........ 49

TABLE35: W ATER ADEQUACY THROUGHOUT THE YEAR ............................................................................................ 50 TABLE36 : CHOICE OFBREEDS BYDISTRICTS INDCA 1 ........................................................................................................ 50 TABLE37: CHOICE OFBREEDS BYDISTRICTS INDCA 3......................................................................................................... 51 TABLE38: STATUS OF PREFERREDBREEDING METHOD IN DCA 1 AND DCA 3 ...................................................................... 51 TABLE39: REASONS FOR BULL PREFERENCE BETWEEN DCA 1 AND DCA 3 ............................................................ 52 TABLE40: COST OF AI SERVICE USING LOCAL SEMEN BY DISTRICTS IN DCA 1 .......................................................... 53 TABLE41: COST OFAI SERVICE USING LOCAL SEMEN BY DISTRICTS INDCA 3 ............................................................................. 54




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TABLE42: COST OFAI SERVICE USING IMPORTED SEMEN BY DISTRICTS INDCA 1 ........................................................................ 55 TABLE43: COST OFAI SERVICE USING IMPORTED SEMEN BY DISTRICTS INDCA 3 ........................................................................ 55 TABLE44: COST OF BULL SERVICE IN DCA 1 AND DCA 3 ......................................................................................... 56 TABLE45: COST OF BULL SERVICE BY DISTRICT IN KSHS .......................................................................................... 56 TABLE46: MAXIMUMNUMBER OFINSEMINATIONS BEFORE CONCEPTION IN DCA 1 ...................................................... 57 TABLE47: M AXIMUM NUMBER OF INSEMINATIONS BEFORE CONCEPTION IN DCA 3 ............. ............. ............. ............ 57 TABLE48: THE CALVING INTERVAL IN THE DAIRY HERD(IN DAYS) IN DCA 1 AND DCA 3 .............. ............. ............ ............. ...... 58 T ABLE 49: AVERAGE MILK PRODUCTION , SALES AND HOME CONSUMPTION IN DCA 1 AND DCA 3 ...... ............. ...... 58 TABLE50: AVERAGE MILK PRICE IN VARIOUS OUTLETS INDCA 1 AND DCA 3 ............................................................................ 59 TABLE51: AVERAGE MILK P RODUCTION , S ALES AND CONSUMPTION BY DISTRICT .................................................. 59 TABLE52: MILK HANDLING PRACTICES BYDISTRICT ............................................................................................................. 61 TABLE53: MILKMARKETINGCONSTRAINTS INDCA 1 ........................................................................................................ 63 TABLE54: MILKMARKETINGCONSTRAINTS INDCA 3 ........................................................................................................ 65 TABLE55: MEAN PRODUCTION OF ON-FARM DAIRY PRODUCTS .............................................................................................. 66 TABLE56: FARMERS WHO NEED SKILLSTO INCREASE PROFITABILITY OF DAIRY ENTERPRISE INDCA 1 ............. ............. .......... 67 TABLE57: FARMERS WHO NEED SKILLSTO INCREASE PROFITABILITY OF DAIRY ENTERPRISE INDCA 3 ............. ............. .......... 68

TABLE58: RESULTS OFFGD ANALYSIS OFCOMMUNITYGROUPS INPROJECTAREA .................................................................... 70 TABLE59: ORGANIZATIONS REGISTERING CATTLE IN DCA 1 AND DCA 3 ................................................................. 73 TABLE60: F ARMERS WITH CATTLE REGISTERED W ITH AT LEAST ONE ASSOCIATION .................................................. 73 TABLE61: THREE COMMON LIVESTOCK DISEASES REPORTED IN DCA 1 AND DCA 3 ................................................ 74 TABLE62: MOST COMMON LIVESTOCKDISEASE BYDISTRICT ................................................................................................ 74 TABLE63: COST OF SECURING ANIMAL HEALTH SERVICES BETWEENDCA 1 AND DCA 3 BYDISTRICT ............. ............. ............. ........ 75 TABLE64: PERMANENT AND CASUAL EMPLOYEES IN AN AVERAGE DAIRY FARM BYDISTRICT INPROJECTAREA ............. ............. .......... 76 TABLE65: AVERAGEDAIRYHERD ANDEMPLOYEES BYDISTRICT ............................................................................................. 77 TABLE 66: DISTRIBUTION OFDAIRYBREEDS BYDISTRICT INDCA 1....................................................................................... 77 TABLE67: DISTRIBUTION OFDAIRYBREEDS BYDISTRICT INDCA 3 ......................................................................................... 78 TABLE68: DISTRIBUTION OF DAIRY STRUCTURE BY BREED INDCA 1 ........................................................................................ 80

TABLE69: DISTRIBUTION OF DAIRY STRUCTURE BY BREED INDCA 3 ........................................................................................ 81 TABLE70: MEANNUMBER OFANIMALS BYBREED INDCA 3 ................................................................................................ 82 TABLE 71: AVERAGE COST OF BUYING A DAIRY COW AT SOURCE IN K SHS ................................................................ 83 TABLE72: DAIRYPRODUCTIONSYSTEM INDCA 1 .............................................................................................................. 84 TABLE73: DAIRYPRODUCTIONSYSTEM INDCA 3 .............................................................................................................. 84 TABLE74: COST OF ZERO GRAZING UNITS IN KSHS .................................................................................................. 85 TABLE75: COST OF OTHER FARM INFRASTRUCTURE IN KSHS ACROSS THE DISTRICTS ............................................. 86 TABLE76: MONTHLY WAGE BILL FOR PERMANENT EMPLOYEES BETWEEN DCA 1 AND DCA 3 ............. ............. ........ 86 TABLE77: MONTHLY WAGE BILL FOR CASUAL EMPLOYEES BETWEEN DCA 1 AND DCA 3 .............. ............. ............. . 87 TABLE78: AVERAGE MONTHLY W AGES IN KSHS ...................................................................................................... 87 TABLE79: CONDITION OF ZERO GRAZING UNIT BETWEEN DCA 1 AND DCA 3 .......................................................... 88

TABLE80: CONDITION OF MILKING SHED BY DISTRICT .............................................................................................. 88 T ABLE 81: GENDER OF HOUSEHOLD HEAD IN DCA 1 AND DCA 3 .......................................................................... 89 T ABLE 82: COMPARISON BETWEEN MEN AND WOMEN ROLES IN DAIRY PRODUCING HOUSEHOLDS ......................... . 91 TABLE 83: GENDER DIVISION OF LABOUR IN DAIRY PRODUCING HOUSEHOLDS ............ .............. ............ ............. ........ 92 TABLE84: HOUSEHOLDS MAKING REGULARSAVINGS FROM THE DAIRY ENTERPRISE IN DCA 1 ............ .............. ............ .... 94 TABLE85: HOUSEHOLDS MAKING REGULARSAVINGS FROM THE DAIRY ENTERPRISE IN DCA 3 ............ .............. ............ .... 95 TABLE86: COMPARISON BETWEENDCA 1 AND DCA 3 IN TERMS OF WHERE HH MEMBER MAKE THEIR SAVINGS ..... .............. . 97




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TABLE87: ACCESS TO CREDIT IN DCA 1 AND DCA 3 ............................................................................................... 99 T ABLE 88: LOAN S IZE IN KSHS ............................................................................................................................. 102 TABLE89: SUCCESS RATE INDCA 1 AND DCA 3 ............................................................................................................. 103 TABLE90: REASONS FOR UNSUCCESSFUL LOAN APPLICATIONS INDCA 1 AND DCA 3 ................................................................ 104 T ABLE 91: R EPAYMENT PERIOD (MONTHS ) IN DCA 1 AND DCA 3 ....................................................................... 105 TABLE92: INTEREST RATE (P . A) IN DCA 1 AND DCA 3 .......................................................................................... 105 TABLE93: INTEREST RATES (%) CHARGED BY TYPE OF LENDER ............................................................................. 107 TABLE94: SIZE AND TERMS OF LOANS INDCA 1 AND DCA 3 .............................................................................................. 107 TABLE95 : TYPE OF COLLATERAL USED IN DCA 1 AND DCA 3 ............................................................................... 109 TABLE96: AMOUNT PAID AT MATURITY KSHS ......................................................................................................... 110 T ABLE 97: AMOUNT PAID AT MATURITY KSHS ....................................................................................................... 110 T ABLE 98: N ATURAL RESOURCE M ANAGEMENT P ROBLEMS BY DITRICT .............................................................. 111 T ABLE 99: S EVERITY OF NRM ACROSS THE PROJECT AREA ................................................................................ 112 TABLE100: ROOF MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD ........................................... 113 TABLE101: WALLMATERIALS BYDISTRICT ANDDCA ....................................................................................................... 114 TABLE102: FLOOR MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD ............ ............. ............. .... 116

TABLE103: W INDOW MATERIAL USED TO CONSTRUCT RESIDENCE OF HOUSEHOLD HEAD ............. ............. ............ 116




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LIST OF ACRONYMS

ABS-TCM - African Breeders Service Total Cattle ManagementAI - Artificial InseminationAIDS - Acquired Immune Deficiency SyndromeCAIS - Central Artificial Insemination StationCBO - Community Based OrganizationsDCA - Dairy Commercialization AreaDIC - Dairy Information CentreDTI - Dairy Training InstituteFGDs - Focus Group DiscussionsFMD - Foot and Mouth DiseaseGDP - Gross Domestic ProductGTZ - German Technical CooperationHIV - Human Immuno Deficiency VirusIFAD - International Fund for Agricultural DevelopmentIFMIS - Integrated Financial Management Information SystemILRI - International Livestock Research Institute

KAGRI - Kenya National Animal Genetic Resource InstituteKARI - Kenya Agricultural Research InstituteKDB - Kenya Dairy BoardKDPA - Kenya Dairy Processors AssociationKEDAPO - Kenya Dairy Producers AssociationKELRI - Kenya Livestock Research InstituteKIHBS - Kenya Integrated Household Budget SurveyKLBO - Kenya Livestock Breeding OrganizationKLMB - Kenya Livestock Marketing BoardLCMIS - Low-Cost Market Information SystemM&E - Monitoring and EvaluationMDGs - Millennium Development Goals

MIS - Management Information SystemsMOLD - Ministry of Livestock Development

NGO - Non-Governmental OrganizationPEV - Post Electoral ViolenceSDCP - Smallholder Dairy Commercialization ProgrammeSOW - Scope of WorkSPSS - Statistical Programme for Social ScientistsSWOT - Strengths, Weaknesses, Opportunities and ThreatsTOR - Terms of ReferenceUNHCR - United Nations High Commission for RefugeesWWS - World Wide Sires




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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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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 theenterprise 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 beforeconception. 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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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 thisthreshold 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 dailyactivity 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.




xiii

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.




1

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 GoalThe 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 PurposeThe 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 ComponentsThe 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, processorand 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




2

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

smallh older 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 legislativedevelopment 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 AssignmentThe 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.




3

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 componentsnamely: 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 Ar ea 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 t ime 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.




4

Table 1: Administrative Areas of DCAs in the Programme Area DISTRICT DCA1 DCA2 DCA3

Location Division Location Division Location Division

BOMETSugumerga, Sigor Kembu Longisa Ndaraweta Bomet Central

Sigor

Sugumerga Kembu Ndaraweta

KISII CENTRALKeumbu, Keumbu Bogiakumu Suneka Bogeka Mosocho

Ibeno Bomorenda EtoraKegati Bosongo Kiogoro Nyakoe

Keumbu Kiogoro/Bogiakumu Mosocho

NYAMIRABonyamatutaChache

Nyamira Nyasiongo

Borabu

BonyamatutaMasaba Nyamira

BogichoraKeera Nyamaiya Makenene Ekerenyo EkerenyoKiabonyoru Ekerenyo

Nyamira Peri-uban Nyasiongo-Mekenene Ekerenyo-Bonyamatuta

NANDI NORTHKapsabet Kapsabet Sigot Kosirai Lolkeringet KabiyetKipture Kalibwoni Kabisaga Kabiyet Kabiemit

Kapasabet-Kipture Sigot-Kabisaga Lolkeringet-Kabiemit

TRANS NZOIAEndebess Endebess Kiminini Kiminini Waitaluk Baraka

Endebess Kiminini Waitaluk

BUNGOMANdalu Tongaren Ndivisi Ndivisi Bukembe Kanduyi

Ndalu Ndivisi Bukembe

LUGARILikuyani Likuyani Lwandeti Matete Lugari Lugari

ChekaliniLikuyani Lwandeti Lugari

UASIN GISHUKapseret Kapseret Moi’s Bridge Soy Sugoi Turbo

Kapseret Moi’s Bridge Sugoi

NAKURURongai Rongai Ngata Njoro Subukia SubukiaLenginet Kabaazi Kabaazi

Rongai Ngata Subukia/KabaaziSource: SDCP




5

Figure 1: Map showing the Area covered by the Survey

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

3.1.1 SamplingThe 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

DistrictDCAs Total

DCA 1 DCA 3Bomet 45 40 85Kisii Central 34 59 93Nyamira 38 63 101Nandi North 32 44 76Trans Nzoia 47 43 90Bungoma 27 43 70Lugari 19 67 86Uasin Gishu 40 50 90Nakuru 39 54 93Total 321 463 784Source: Baseline Survey, April, 2009




6

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 PresentationThe 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




7

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 Sour ces of D ata and Coll ection T echni ques

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 Sour ces

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.




8

3.7 Literatur e 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 theliterature 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 I nformants

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 Dair y 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 thatwas 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.




9

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 thecommunity; 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.

i i i) . 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 theinvolvement 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 guidingtrade of milk and other dairy products in Kenya.

v). M arket 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 F ocus Group Di scussions and Key In for mant I nterviews

The study team conducted one focus group discussion in each of the nine districts with a limited group of milk




10

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 F ield Visits

The study team comprised local enumerators one from each district in the project area to administerquestionnaires. 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 Studi es

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 deviantswere 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.




11

4 STUDY FINDINGS

4.1 Nutritional StatusTo determine the nutritional status in each district within the programme area, the study used theresults 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 theWorld 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 updatethe 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 thenutritional 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 themedian/WHO NCHS reference population growth standards. A child falling below -2 standarddeviations (-2 and below -3 standard deviations (-3) is severely malnourished. In the WHO/NCHSreference 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 deviation s from the median of the reference population in terms of height-for-age is considered tooshort for his/her age or stunted (chronic malnutrition). A child falling below -3 is severelymalnourished. Underweight (WAZ) weight-for-age is a composite index for weight for height andheight for age and thus does not distinguish between acute malnutrition (wasting) and chronicmalnutrition (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 27,223Kisii 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 29,018Uasin 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 31,453Bungoma 21.2 2.7 32.3 16.8 4.9 0.0 60,696Lugari 2.1 0.0 23.1 12.9 0.0 0.0 12,992Source: KNBS Household Welfare Survey, December 2005




12

(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 43,139Kisii 7.5 2.6 39.7 19.5 0.0 0.0 26,253Trans Nzoia 9.1 3.5 39.3 27.1 2.4 0.0 28,336Uasin 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 46,446Bungoma 21.9 3.1 24.1 13.7 6.0 0.0 72,344Lugari 3.3 0.0 34.7 9.7 0.0 0.0 17,279Source: 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 fromthe 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.




13

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 stuntedand 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.




14

4.2 HouseholdsBelow 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 dairyfarming because they help to tailor interventions to match the circumstances of dairy farmers.

4.3 Level of EducationThe 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.




15

Table 4: Highest Education level of household heads by District and DCAHighest educational level attained

District NonePrimaryeducation

Secondaryeducation

College/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 SizeThis 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.




16

Table 5: Size of household by District and DCADistrict 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 2.478Uasin 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 HeadThis 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 programarea 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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Figure 3: Main Occupation of Household Head in DCA 1


Figure 4: Main Occupation of Household Heads in DCA 3


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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Table 8: How much land is available to this family?District N Mean Std.

DeviationBomet 45 4.4 2.6Kisii Central 34 2.3 2.0

Nyamira 38 2.4 1.6 Nandi North 34 3.2 3.3Trans Nzoia 49 4.2 4.3Bungoma 29 5.1 4.7Lugari 19 5.0 4.3Uasin Gishu 42 4.1 3.9

Nakuru 39 2.1 1.9Total 329 3.6 3.4

Bomet 42 4.4 2.4Kisii Central 60 4.5 2.5

Nyamira 63 2.2 1.3Nandi North 45 4.6 3.7Trans Nzoia 45 3.7 4.3Bungoma 43 3.6 2.8Lugari 68 4.3 3.9Uasin Gishu 52 5.4 3.9Nakuru 54 2.4 2.2Total 472 3.9 3.3


4.7 Land OwnershipThe 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 1Do you own this farm?

District Yes No TotalBomet 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 3Do 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 2009The 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




21

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 landDCA Area Reasons why you do not own land? Total

District TenantCommunalland use Family

Haveno titledeed

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%

DCA3 District TenantCommunalland use Family

Haveno titledeed 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%


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


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

DCA Area District Fodder Pasture Crops Buildings OthersTotal

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 4.01Lugari 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 3.29Source: Baseline Survey, April 2009




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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 3.93Source: Baseline Survey, April 2009

4.9 Milking HerdThis 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





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Table 14: Average size of the milking herd by breed by District in DCA 1District Friesian

cows inmilk

Jerseycows inmilk

Guernseycows inmilk

Crossbreedcows inmilk

Localcows inmilk

Total

DCA1 Bomet 0.1 0.31 0 0.71 0.37 1.5Kisii 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 2.1Trans Nzoia 0.06 0 0 1.4 0 1.5Bungoma 0.43 0 0.27 0.38 0.22 1.3Lugari 1.73 0 0 0.41 0.14 2.3Uasin Gishu 0.64 0.02 0 0.47 0.06 1.2

Nakuru 1.63 0 0.05 0.05 0 1.7Total 5.69 0.50 0.41 6.52 1.05 14.2


Table 15: Average size of the milking herd by breed by District in DCA 3District Friesian

cows inmilk

Jerseycows inmilk

Guernseycows inmilk

Crossbreedcows inmilk

Localcows inmilk

Total

DCA3 Bomet 0.76 0 0.04 0.74 0 1.54Kisii 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 1.3Trans Nzoia 0.33 0.11 0.04 0.67 0.04 1.19Bungoma 0.2 0.1 0 0.44 0.44 1.18Lugari 0.40 0.01 0.1 0.62 0.1 1.23Uasin Gishu 1.00 0.02 0.02 0.58 0.16 1.78

Nakuru 1.32 0 0.02 0.08 0 1.42Source: Baseline Survey, April 2009

4.10 Milk ProductionThe 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.




25

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 DistrictDCA1 DCA3

District Mean Std. Deviation Mean Std. DeviationBomet 3.9 2.98 9.1 3.97Kisii 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 5.60Trans Nzoia 7.9 5.22 6.3 4.80Bungoma 9.4 5.47 5.2 3.73Lugari 14.2 7.90 9.6 5.99Uasin Gishu 7.3 5.65 14.0 9.45

Nakuru 13.3 9.06 9.1 6.41Total 8.5 6.81 9.2 6.39


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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Figure 7: Distribution of milk production across the SDCP Area


4.11 Farm RecordsThis 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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Table 17: Proportion of households keeping Farm Records in DCA1 and DCA 3Proportion of farmers that kept farmrecordsDistrict DCA 1 DCA 3 TotalBomet 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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Table 18: Type of farm records kept by farmers in DCA 1 and DCA 3 by District

DistrictMilkProduction Breeding Health Sales

Leasingrecords Total

DCA1Bomet 75% 25% 0% 0% 0% 100%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%

DCA3Bomet 86% 14% 0% 0% 0% 100%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%


4.12 Household WelfareThe 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.




29

Figure 8: Mean Monthly Household Expenditure

Transport6%

Others5%

Health10%

Food35%

Education44%

Mean Monthly Household ExpenditureKshs 21, 423


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 1Average 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 12,175Commercial farming 5,000 3,000 - - 2,500 10,500Subsistence farming 7,203 1,002 3,265 383 245 12,098Total 6,698 896 5,305 476 356 13,731

KisiiCentral

Business 6,500 250 12,500 1,750 - 21,000

Salaried employment 8,667 2,667 20,000 667 100 32,100Dairy farming 4,729 1,043 4,357 1,057 2,600 13,786Commercial farming 3,000 200 200 100 - 3,500Subsistence farming

5,750 1,900 6,725 1,225 2,625 18,225Mixed farming 7,076 1,882 15,472 2,650 94 27,175Total 6,424 1,635 11,930 1,851 900 22,740

Nyamira Business 8,000 5,000 10,000 2,000 - 25,000Salaried employment 5,000 1,000 3,000 4,000 - 13,000Dairy farming 10,013 1,650 19,313 5,100 - 36,075Commercial farming 6,965 2,395 14,955 1,730 - 26,045Subsistence farming 5,900 7,200 10,240 3,000 - 26,340Total 7,349 2,841 14,157 2,822 - 27,168

Nandi North

Business 1,000 - 2,000 1,200 - 4,200

Salaried employment 4,000 - 2,500 1,050 - 7,550Dairy farming 3,239 52 2,820 583 - 6,693Commercial farming 4,000 375 3,875 300 - 8,550Total 3,317 90 2,912 597 - 6,915

Trans Nzoia

Business 6,000 1,000 - 1,000 - 8,000

Salaried employment 15,000 1,600 40,000 4,200 2,100 62,900Dairy farming 5,933 645 3,263 931 587 11,359Commercial farming 6,771 1,047 7,447 1,426 507 17,198Subsistence farming 4,500 400 1,833 - - 6,733

Total 6,614 916 6,526 1,287 545 15,888Bungoma Business 3,000 1,000 - 800 - 4,800

Dairy farming 7,771 4,840 21,363 3,779 13 37,766Commercial farming 2,500 3,000 25,000 2,000 - 32,500Subsistence farming 350 3,000 7,222 15,000 - 25,572Total 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 9,500Salaried employment 13,000 1,100 3,925 2,000 5,000 25,025

Commercial farming 7,625 350 8,500 4,850 7,000 28,325Subsistence farming 9,286 371 20,714 3,471 9,143 42,986Casual labourer 1,000 300 7,000 - 3,000 11,300Total 8,750 506 11,294 3,133 6,572 30,256

UasinGishu

Business 3,457 371 1,914 671 2,143 8,557

Salaried employment 6,000 500 1,250 2,000 1,500 11,250Dairy farming 10,567 2,463 4,687 1,955 863 20,535Commercial farming 11,625 3,125 4,000 2,300 250 21,300Subsistence farming 20,745 8,455 7,209 1,036 2,382 39,827Total 12,036 3,745 4,654 1,503 1,491 23,429

Nakuru Business 6,000 2,000 4,500 5,000 2,000 19,500Salaried employment

6,500 2,375 6,500 4,375 2,750 22,500Dairy farming 4,500 2,250 2,842 1,750 1,750 13,092Commercial farming 4,000 3,000 2,000 3,000 1,500 13,500Subsistence farming 5,200 1,750 6,000 3,100 1,950 18,000Total 5,429 2,000 5,553 3,343 2,100 18,425


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 TypeAverage Household Expenditure by Type, Occupation, District in DCA3

District Occupation Food Health Education Transport Others Total

Bomet Business 2,500 200 1,000 200 - 3,900Salariedemployment

8,000 5,000 30,000 4,000 - 47,000

Dairy farming 7,462 1,100 3,254 1,077 346 13,238Commercialfarming

7,250 485 9,300 1,625 1,025 19,685

Subsistencefarming

8,097 1,066 13,009 714 664 23,550

Total 7,664 1,096 9,592 992 564 19,907KisiiCentral

Business 4,281 6,094 16,031 1,381 41 27,828

Salaried

employment

3,260 2,140 14,200 1,400 120 21,120


2,875 - 5,375 975 200 9,425

Mixed farming 4,000 400 3,410 780 - 8,590Casual labourer 2,000 - - 250 - 2,250Total 3,746 4,719 11,977 1,117 69 21,628

Nyamira Business 6,167 378 3,536 1,122 - 11,203Salariedemployment

7,000 - 5,000 1,500 - 13,500

Dairy farming 8,857 2,114 4,743 1,336 - 17,050

Commercialfarming

6,889 931 3,326 1,397 - 12,543

Subsistencefarming

4,000 - 500 100 - 4,600

Total 6,856 874 3,528 1,291 - 12,550 Nandi North

Business 2,857 240 717 829 - 4,642

Salariedemployment

9,600 500 11,000 2,600 - 23,700

Dairy farming 3,853 206 3,615 518 - 8,191Commercial

farming

3,929 500 5,131 1,157 - 10,717

Subsistencefarming

5,400 130 1,266 1,040 - 7,836

Mixed farming 6,000 5,000 5,000 1,000 - 17,000Total 4,619 401 4,017 998 - 10,034

Trans Nzoia

Business 10,875 2,800 19,750 15,088 12,500 61,013




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Average Household Expenditure by Type, Occupation, District in DCA3District Occupation Food Health Education Transport Others Total

Salariedemployment

15,000 1,100 5,500 1,000 500 23,100

Dairy farming 9,000 1,507 29,520 1,993 667 42,687

Commercialfarming 9,889 722 19,611 1,539 522 32,283

Subsistencefarming

9,283 4,983 17,667 6,958 2,183 41,075

Total 9,736 2,436 21,936 4,514 2,188 40,810Bungoma Business 5,500 1,500 6,500 650 - 14,150

Salariedemployment

6,000 3,000 12,000 2,500 - 23,500


7,000 4,000 500 2,000 - 13,500

Subsistencefarming 6,000 8,000 13,333 450 - 27,783Mixed farming 12,000 1,500 7,667 2,500 - 23,667Total 7,156 2,765 5,535 1,587 0 17,043

Lugari Business 9,014 1,257 4,786 2,443 3,071 20,571Salariedemployment

17,385 4,494 20,546 5,500 4,692 52,617

Dairy farming 35,000 3,033 8,833 3,867 2,167 52,900Commercialfarming

15,000 5,000 18,000 2,000 10,000 50,000

Subsistence

farming

8,113 2,486 16,103 1,573 2,543 30,816

Total 12,516 2,834 15,160 2,637 3,093 36,240UasinGishu

Business 6,725 3,063 3,163 4,263 2,825 20,038

Salariedemployment

5,750 2,000 7,700 600 450 16,500


5,025 1,950 3,500 2,950 750 14,175

Subsistencefarming

7,286 1,614 6,362 2,629 2,810 20,700

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

Salariedemployment

6,875 2,000 7,250 5,313 2,100 23,538


5,000 10,000 2,000 8,000 2,000 27,000




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Average Household Expenditure by Type, Occupation, District in DCA3District Occupation Food Health Education Transport Others Total

Subsistencefarming

5,467 2,367 6,567 2,967 1,733 19,100

Total 6,064 2,638 6,404 3,606 1,783 20,496

Source: Baseline Survey, April 2009This 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




35

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 3.5Kisii Central 40.9 134Nyamira 5.3 2.5Nandi North 0 0Trans Nzoia 180 23.1Bungoma 0.04 0.5Lugari 0 24.8Uasin Gishu 150 42.8Nakuru 50.4 36.3Total 56.2 32.4


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 dayDistrict Minimum Maximum Mean Std. DeviationBomet 0 100 1.77 11.093Kisii Central 0 1,700 136 262.878Nyamira 0 150 3.56 17.753Nandi North 0 0 0.00 0.000Trans Nzoia 0 6,000 164.12 860.658Bungoma 0 20 0.29 2.274Lugari 0 600 19.89 92.310Uasin Gishu 0 3,000 94.23 370.720Nakuru 0 500 43.88 57.412Total 0 6,000 49.89 340.017


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.




36

Figure 10: Main Animal feeds in the Project Area


This study found that there was little difference between DCA 1 and DCA 3 in terms of the main feedsources as shown in Table 24 below.

Table 23: Main livestock feed in DCA 1 and DCA 3

Main feed for livestock

Pastures Napier grass HayDCA 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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Figure 11: Main animal feeds by District


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

Dca Area

Napier Grass(load)

MaizeStover(load) Calliandra Mulberry Lucaenia

On-FarmFormulation(kg)

CommercialFeeds (kg)

OtherFeeds(kg)

DCA1 7.46 8.24 N/A N/A 1.00 1.54 9.46 1.99DCA3 3.44 13.91 N/A -N/A 1.00 2.04 4.36 4.82Total 4.82 9.80 N/A -N/A 1.00 1.86 6.73 3.50


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 3DCA

AreaNapierGrass

MaizeStover Calliandra Mulberry Lucaenia

On-FarmFormulation

CommercialFeeds

OtherFeeds

TotalCost

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


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

District

Quantityof napiergrass

Cost ofnapiergrass(Kshs)

Quantityof Maizestover

Cost ofMaizestover(Kshs)

Cost of on-farmformulation (Kshs)

Quantity ofon-farmformulation

Cost ofcommercial feeds(Kshs)

Quantity ofcommercial feeds

TotalCost inKshs

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


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


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


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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Table 27 : Reasons why farmers don’t use supplements in DCA 1 by District

Reason BometKisiiCentral

NandiNorth

TransNzoia Bungoma Lugari

UasinGishu Nakuru Total

Don't know how to grow

fodder 0% 0% 0% 0% 0% 0% 8% 0% 2%Don't have access to fodderseeds 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 tomanage 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%


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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Figure 14: Feed Contingency Measures in DCA 1


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


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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Table 30: Feed contingency measures in DCA 3

Feed contingency measures taken to ensure milk production throughout the year

District BometKisiiCentral Nyamira

NandiNorth



Feed conservation 72% 69% 44% 98% 72% 28% 97% 80% 31% 66%Contracting otherfarmers 13% 0% 2% 0% 5% 2% 0% 8% 37% 7%Purchasing fromoutside farm 10% 22% 54% 0% 21% 23% 0% 8% 31% 20%Moving animals togreener 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 ProductionTo 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


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





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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


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




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Table 31: M ain water sources in DCA 1 and DCA 3 during wet season

The main source of water during the wetseason

DCA 1 DCA 3 TotalRiver 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%


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 TotalRiver 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%





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Table 35: Water adequacy throughout the year

District

Is the water adequatethroughout the year?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%


4.21 Choice of Animal BreedsThis survey found that 83% of the farmers in DCA 1 used milk yield as the most importantconsideration in choosing the preferred dairy breed. There was wide disparity between districts on thisaccount. 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

DistrictMilkyield

Growthrate

Diseaseresistance

Marketvalue

Bodyweight

Feedingbehavior 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%


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




51

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 breedsother 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

DistrictMilkyield

Growthrate

Diseaseresistance

Marketvalue

Bodyweight

Feedingbehavior 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%


4.22 Preferred Breeding MethodsThe 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

DCAs

Which breeding method do youmostly prefer

AIBull

service Local bull TotalDCA 1 41% 9% 50% 100%

DCA 3 46% 10% 44% 100%Total 43% 10% 47% 100%

Source: Baseline Survey, April 2009This 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




52

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


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 TotalHigh 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%





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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 CostsAvailability 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: C ost of AI service using local semen by districts in DCA 1

The cost of using AI local semen in DCA 1

District Minimum Maximum MeanStd.Deviation

Bomet 600 1,500 1,050 636Kisii Central 600 2,500 878 467Nyamira 600 700 643 53Nandi North 600 700 609 30Trans Nzoia 600 700 680 45Bungoma 600 3,000 1,222 710Lugari 750 1,000 943 97Uasin Gishu 600 1,200 910 225Nakuru 600 1,000 681 74Total 600 3,000 770 317





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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


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 168Nakuru 600 3,000 845 398

Total 800 1,500 1,013 217Source: 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 betweenthese 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


Bomet 800 6,000 2,500 2,386

Kisii Central 500 2,000 885 321Nyamira 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 321Source: 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. DeviationBomet 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 632Source: 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




56

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 Kshs113 to access bull service compared to their counterparts in DCA 3 who paid Kshs 172 as shown in Table 44

below.

Table 44: C ost of bull service in DCA 1 and DCA 3

DCAs Minimum Maximum Mean Std. DeviationDCA 1 Free 600 113.30 170.367DCA 3 Free 700 172.43 171.418Total Free 700 145.36 173.296


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: C ost of bull service by District in KshsDistrict Minimum Maximum Mean Std. DeviationBomet Free 200 12.50 44.859Kisii Central Free 300 200.00 64.327Nyamira Free 600 340.52 147.034Nandi North Free Free .00 .000Trans Nzoia Free 700 152.14 212.888Bungoma Free 500 290.42 157.817Lugari Free 400 225.40 94.984Uasin Gishu Free 500 85.48 156.652Nakuru 200 400 272.73 64.667Total Free 700 145.36 173.296


4.25 Breeding EfficiencyOne 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




58

Table 48: The calving interval in the dairy herd (in days) in DCA 1 and DCA 3

DCA Area District Minimum Maximum Mean Std. DevDCA1 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 150Source: 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

DCAsMilk production inlitres/day

Milk sold inlitres/day

Home consumptionin litres/day

DCA 1 Mean 8.84 6.03 2.93DCA 3 Mean 9.81 6.38 3.26Total Mean 9.39 6.23 3.11


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




60

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 outletsKey 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 practicesThe 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 determinecompliance 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


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 TotalBomet 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%


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 anexperienced 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 –




63

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 Boil ing M ethod: 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.

M atch 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.

Th e 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 ConstraintsAnalysis 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


NandiNorth



Harassment by localauthorities

3% 12% 0% 0% 0% 0% 0% 0% 5% 3%

Harassment by KDBinspectors

0% 6% 0% 0% 0% 0% 0% 3% 3% 2%

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 standardmeasurements for milk

0% 30% 3% 0% 0% 0% 7% 3% 0% 5%

Bad roads 0% 0% 0% 0% 0% 0% 7% 0% 0% 0%


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 ?

District – DCA 3 BometKisiiCentral Nyamira

NandiNorth



Harassment by localauthorities

3% 50% 0% 0% 0% 0% 2% 0% 0% 6%

Harassment by KDBinspectors

5% 0% 0% 0% 0% 0% 7% 11% 2% 3%

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%Corruptmanagementcommittees

0% 0% 0% 0% 0% 0% 0% 2% 0% 0%

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 standardmeasurements formilk

0% 0% 0% 0% 0% 0% 2% 0% 0% 0%

Unable to satisfymarket demands

0% 8% 0% 0% 0% 0% 0% 0% 0% 1%

Bad roads 0% 0% 0% 0% 0% 2% 0% 0% 0% 0%

Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%


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 ProcessingThis 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


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

DistrictVolume of

mursikVolume of

yoghurtVolume of

gheeVolume of

malaBomet 1.88Kisii Central .50 6.00 10.75Nyamira .50 3.57

Trans Nzoia 7.50 10.00 7.50Bungoma 3.62Lugari 0.46Uasin Gishu 5.75 .250Nakuru 6.00Total 3.18 7.00 .50 5.81


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 animalhusbandry 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


NandiNorth



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%


In DCA 3, 80% of the farmers required animal husbandry related skills to increase profitability of the dairyenterprise 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


NandiNorth



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%


4.32 Types and Organization of Community GroupsThe 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

trict Group Membership Objectives Management BusinessDevelopment

Record Keeping Finances Training Areas

Kisii Central KeumbuCommunityMilk Vendors

10 men13 women

a) Increase milk marketb) Improve living standards of

members.c) Create employment

opportunities.

Decision making is throughconsensus among members

The group has notreceived any businesstraining

Membersrecords andminutes ofmeetings

Monthly membersubscriptions

They identified the need to betrained in milk quality controland using testing kits.

Nandi North Aganwet CBO(Nandi EthnicGroup)

a) Improve the living conditionsand incomes of the members

b) Collecting and marketing milkfor members

The group has 7 committeemembers of which 5 aremale and 2 are femaleDecision making is byconsensus of members inthe location- Chemnoet

The group has notreceived any trainingin managing dairyenterprise

They alwaysmake recordsfor the groupactivities

They havemonthly meetingswhere they collectmembersubscriptions

Improved management andanimal husbandry to increasemilk production

Trans Nzoia Mbiria SelfHelp Group(Mixedgroup)

14 men22 women

Turkana,Luyhas, Teso,Luos

Assist members to get dairyanimalsSome members have crossbred cows, others don’t havecows and some don’t evenhave farms.They have applied forfunding but still awaitingfundsOther NGOs they have beennetworking some years backis V1. V1 has been supplyingthem with seed of Caliandriaand Sesbania etc. it has alsobeen providing them withtraining on Agro-forestry.

Elections are held once peryear.

OfficialsAntony Lussala –ChairpersonFrancis Wafubora Vice-ChairpersonFred Masika – AssistantSecretaryFlorence Nasimiyu – Treasurer.Jerida Wasike – Dorcas Nasimiyu – Priscilla Nafula-Alphonse Wanyama

The group is nottrained in businessmanagement

The groupkeeps theminutes of theirmeetings as theonly consistentrecords

Merry –Go-RoundShs 50 monthlycontribution permember.

The registrationfee is Kshs 100

Most farmers are not willing topay for training because thereare many NGOs giving hand-outs during trainings e.g SDCP,World Vision, NAYAP

KoschinGroup

4 men22 women

Koschin means to agree or tolove one another.To reduce poverty amongmembers.It has one ethnic groupKalenjins.The group is registered in theministry of social service.They network with socialservice and SDCPSDCP has facilitated trainingof group dynamics andfarming as a business.Future plans of group is to

Decisions are made bymembersBudget is done byexecutive committeeThey have groupconstitution andmembers know groupobjectivesMeetings are done onceper month

SDCP has facilitatedtraining of groupdynamics and farmingas a business.

Future plans of groupis to purchase a plotand expand farming asa business.

They keeprecords,documentsand minutesduringmeetings.

Merry-go-roundand dairy cows

Most farmers expressed interestin being trained on farming as abusiness.




71



purchase a plot and expandfarming as a business.

Gaa-SeiyotGroup

40 women

Kalenjin andLuyha

Improve living standard ofmembers.Improve girl child education.Engaged in business projecte.g dairy farming as abusiness, selling milk etc.To assist orphans especiallyPLWHA

Election is done after 2yearsThey meet weekly withexecutive and the rest ofmembers.

They have by-laws and theydon’t network with any

organizations apart fromSDCP.Mary Bel – ChairladyRosebela Kole – SecretaryAnn Melly – Vice –SecretaryEgla Sirowey – TreasurerFelustud Boem- CommitteeEmily Tirop – Committee

SDCP has facilitatedtraining on dairymanagement, plantingfodder and grass.Given 4 dairy goats

They haveminutes on alltheir activities

Registration feeKshs 200

Monthlycontribution Kshs100 per member

Managing the dairy farming as abusiness and how to increasemilk production were the skillsthat most members felt theyneeded.

Bungoma Board ofEvangelistSelf HelpGroup

5 Men27 Women

Income generation.Promote agriculturalproduction.Improve milk production andmarketingBuy dairy cows for membersin rotation.Assist orphans, widows andpeople with HIV/AIDS.

The organization has awritten constitution thatacts to guide groupactivities; it showsschedules for activities,explains disciplinarymeasurers and successionof group membership. Itguides in borrowing andcontribution, leadership androles of members

Pasture production,livestock production,Crop production,

The recordskept areattendancerecords,financialrecords,productionrecords,minutes of themeeting, salesand purchasesrecords andhealth records,every decisionmade isfollowed up.

The use dairyanimals ascollaterals tosecure loans

Merry –go –groundand memberscontribution

The group makesmilk as in kindcontributions

Feed formulation and fodderproduction

Education on milk handling,fund raising, trading in farmproduce, micro financing,mobilizing ,training andawareness creation

SDCP has offered trainings onbiogas production, livestockproduction, pasture production,resource mobilization andleadership, value addition,disease control and recordkeeping.

Lugari InyangeWomenGroup

9 Men15 Women

Mobilize funds to buy cattlefor membersAssist members to payschool fees, social and moralsupport

They keeprecords of AIservices, salesand productionrecords.

Dairy farmers have a bigproblem in marketing. Theyclaim markets are seasonal,during dry seasons markets areavailable and prices are high butin wet seasons there are nomarkets leading to a lot oflosses as they don’t havecoolants for preserving milkinstead they dispose at a throw




72



away prices.Uasin Gishu Transparent

Sirende SelfHelpWaitaluk

25 members Dairy farming of cows and sheepImprove living conditions ofmembersPlan to buy a vehicle totransport milk.

Elections are done yearlyMembers know by laws

.OfficialsJohn Njenga – ChairpersonJoseph Macharia – SecretaryMilcah Aumot – Treasurer

Mary Wamboi – CommitteeRoselyne Kae – CommitteeDaniel Ng’ang’a -Committee.

The group did nothave any managementtraining, valueaddition and livestockmanagement

Maintain theminutes of theirgroup meetingsas a record ofthe decisionsthat they make.

They collect amonthlysubscription fromeach member tosupport groupactivities.

Transporter of milk unreliableLack of stable market(unorganized market)Lack of enough leadership skillsand managementThey need training on valueaddition and management oflivestock

FusieeWidows SelfHelp Group

16 members Improve living conditions ofmembers

Started 25/06/2006registered at social servicesministry. The group has alsobank account

OfficialsElizabeth Makhoka – ChairladyJane Ngara – SecretaryRuth Weruga – TreasurerNiva Luvai – CommitteePenina Wafula – Committee

Lack of adequatecapital to increaseanimals and dopaddocking

They get 5 litresof milk per dayand project tohave 10 litresby end ofseason

Registration fee is100/=Share fee is 100/=Merry-go-roundamong membersMembers alsohave dairy cows.

.

Animal disease controlThey cack of enough feeds andpastures for animalsThey need management skills ofanimals and marketinformationThey need training on dairygoats

Nakuru Rongailivestockmanagement

20 members To improve the economicwelfare of their membersthrough dairy farming bymobilizing resources from

members

Groups decisions are madeby consensus

SDCP has facilitatedtraining of groupdynamics and farmingas a business.

Minutes ofgroup meetingsas the onlyregular records

Lack creditfacilities with milkas collateral

Feed conservationWater conservation structuresHeat detection

Source: Analysis of Focus Group Discussions, April 2009




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4.33 Size of the GroupsThe 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 CowsThe 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: O rganizations registering cattle in DCA 1 and DCA 3

If cattle are registered,which organization?

DCAs Total

DCA 1 DCA 3Breeders Association 5 7 12Dairy Recording System 0 4 4Kenya Stud Book 1 1 2Total 6 12 18


Surprisingly, the largest number of dairy animals was registered in Kisii Central and Bungoma Districtwhile 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

District Are your cattle registeredwith 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%


4.35 Animal Health Management and Delivery

Foot and Mouth Disease (FMD) and East Coast Fever (ECF) are the most common diseases inthe 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: T hree 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%


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 ” re port usually

maintained by veterinary office and that forms the basis for further follow-up.

Table 62: Most common Livestock Disease by District

Disease BometKisiiCentral Nyamira

NandiNorth



Foot and MouthDisease (FMD) 100% 88% 1% 84% 45% 64% 76% 91% 11% 60%Lumpy SkinDisease (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 riskIn 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 farmersreported 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 monthThis 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

District Transport TimeRepeat

servicesOthercosts Transport Time

Repeatservices

Othercosts

Bomet 174.0 17.3 135.7 0.0 328.3 10.6 2.4 2.5Kisii 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 1.9Trans Nzoia 180.0 45.7 175.8 0.0 110.0 198.8 1.4 0.0Bungoma 99.3 39.3 629.3 1.9 134.7 23.7 803.1 0.0Lugari 102.6 0.0 0.0 0.0 135.8 28.4 20.9 11.9Uasin 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 0.4Total 129.1 67.3 119.7 61.2 145.8 164.6 121.8 10.9


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 theymay 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

DistrictPermanentEmployees

CasualEmployees

PermanentEmployees

CasualEmployees

Bomet 0 1.0 0 1.0Kisii Central 1.0 1.8 1.1 1.0Nyamira 1.0 1.2 1.0 1.0Nandi North 1.1 0 1.0 1.0Trans Nzoia 2.0 1.2 1.2 1.1Bungoma 1.2 1.3 1.3 1.0Lugari 1.0 1.2 1.1 1.4Uasin Gishu 1.0 1.1 1.1 1.9Nakuru 1.1 1.0 1.0 0Total 1.1 1.3 1.1 1.4


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.




79

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 classesof 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.




80

Table 68: Distribution of dairy structure by breed in DCA 1



UasinGishu Nakuru

NandiCentral

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



UasinGishu Nakuru

NandiCentral

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%





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

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


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 KshsAverage cost of dairy cow at sourceDistrict DCA1 DCA 3Bomet 14,289 23,138Kisii Central 27,941 20,870

Nyamira 21,434 22,532

Nandi North 23,475 28,833Trans Nzoia 29,489 26,558Bungoma 23,278 20,919Lugari 31,632 25,209Uasin Gishu 32,692 27,620

Nakuru 40,385 38,222Total 26,951 25,983


4.40 Production SystemOne 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 1District No

StructuresSemi ZeroGrazing

ZeroGrazing

Total

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%


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 3District No

StructuresSemi ZeroGrazing

ZeroGrazing

Total

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%





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4.41 Cost of Zero GrazingThe 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 KshsCost of Zero Grazing Structures

DCA1 DCA3District Mean Std.

DeviationMean Std.

DeviationBomet 15,000 . 12,500 3,535.5Kisii 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 9,545.9Trans Nzoia 20,400 27,718.6 23,143 25,863.1Bungoma 11,375 16,611.7 12,332 17,333.3Lugari 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 26,341.3Total 23,273 28,400.8 15,369 19,632.8Source: Baseline Survey, April 2009

4.42 Farm InfrastructureOther 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 districtsDCAArea

District Cost offeed store

Cost ofmilking shed

Cost ofcrush

Total

DCA1 Bomet 742 485 975 2,202Kisii Central 4,784 8,667 6,792 20,242Nyamira 7,125 2,200 9,325Nandi North 5,000 1,875 6,875Trans Nzoia 9,000 6,000 1,392 16,392Bungoma 5,167 4,182 1,838 11,186Lugari 16,750 16,750Uasin Gishu 16,635 15,439 4,628 36,702Nakuru 22,200 1,629 1,167 24,996Total 11,849 4,406 2,728 18,984

DCA3 Bomet 3,621 2,488 679 6,788Kisii Central 1,594 2,002 1,331 4,928Nyamira 3,833 1,748 1,500 7,081Nandi North 5,640 1,686 7,326Trans Nzoia 15,955 4,500 1,716 22,170Bungoma 3,250 2,627 2,000 7,877Lugari 10,013 3,350 1,150 14,513Uasin Gishu 7,458 7,500 2,940 17,898Nakuru 23,188 2,207 2,000 27,394Total 10,298 2,499 1,431 14,229


4.43 Cost of LabourThe 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. DeviationDCA 1 600.00 12,000.00 2,625.37 1,656.06DCA 3 700.00 7,000.00 2,058.39 1,016.78Total 600.00 12,000.00 2,239.28 1,280.09





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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. DeviationDCA 1 100 6,300 2,256.86 1,682.410DCA 3 70 15,000 2,005.71 1,902.920Total 70 15,000 2,095.92 1,824.743


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 dueto prevailing employment opportunities and the prevailing wage rates.

Table 78: Average Monthly Wages in Kshs

District

Monthly wage billfor permanent

employees

Monthly wagebill for casual

employees

TotalMonthly

Wage BillBomet 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 5,061Lugari 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


4.44 Condition of Milking shedThe 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

DCAs

Condition of zero grazing unit Total

Good Average PoorDCA 1 31

25%77

62%15

13% 123

DCA 3 5633%

8149%

2918% 166

Total 8730%

15855%

4415% 289


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 PoorBomet 3 24 1 28Kisii Central 25 14 5 44Nyamira 0 45 1 46Nandi North 11 1 0 12Trans Nzoia 7 8 4 19Bungoma 11 14 0 25Lugari 4 4 1 9Uasin Gishu 11 6 1 18Nakuru 5 27 28 60Total 77 143 41 261


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




91

The data collected showed that a wom an’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 Men1. Preparing the milking equipment2. Milking3. Preparing breakfast4. Preparing children for school5. Washing utensils6. Cleaning the house7. Collecting fodder8. Feeding the cows9. Cleaning the cow shed10. Taking milk to the collection centre11. Preparing lunch12. Weeding napier grass13. Work in the shamba14. Fetching firewood15. Fetching water16. Washing clothes17. Preparing supper18. Assisting children with their homework19. Preparing children to go to sleep

1. Taking milk to the collection centre2. Cutting grass or fodder3. Feeding dairy cows4. Weeding napier grass5. Watering the dairy animals6. Taking a nap or siesta7. Taking a walk8. Tethering the animals9. Watching the news10. Plucking tea leaves11. Cleaning the cow sheds12. Checking on the animals13. Taking tea leaves to the buying centre14. Going to the shopping centre to have a

chat15. Visiting neighbours to socialize16. Removing stumps17. Looking for wage employment

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 neighbo rs 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. Theexception 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 householdsDairy task Performed by Task performedmainly by gender

How rigid is thedivision of labour

1) Napier grass management Both men & women Men Flexible

2) Crop residue harvesting Both men & women Men Flexible3) Fodder conservation Both men & women Men Flexible4) Spraying and disease control Men Men Rigid5) Artificial Insemination Men Men Rigid6) Feeding dairy cows Both men & women Both men &

womenFlexible

7) Watering the animals Both men & women Both men &

women

Flexible

8) Grazing animals Men Men Rigid9) Treatment of sick animals Men Men Rigid10) Milking Women Women Rigid11) Milk Marketing Both men & women Men Flexible12) Cleaning sheds Both men & women Women Flexible13) Milk processing Both men & women Men Flexible14) Management of hired labour Both men & women Men FlexibleSource: 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




93

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. W omen’s lack of control overland 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 implica tions 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.




94

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 maincommunities.

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


NandiNorth



Yes 11% 38% 34% 34% 17% 67% 37% 15% 46% 31%

No 89% 62% 66% 66% 83% 33% 63% 85% 54% 69%


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.




95

Table 85: Households making regular savings from the dairy enterprise in DCA 3


NandiNorth



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%


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


Figure 30: Preferred Methods of Savings




97

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 w here HH member make their savingsWhere is the HH

member makingtheir savings?

DCAs Total

DCA 1 DCA 3Local trader

6 15 21Group 23 39 62Cooperative 6 20 26Savings account 78 82 160Home savings 1 3 4Total 114 159 273


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

a) Emergencies and investment opportu ni ties that may ari se any timeThe 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 ConsumptionHouseholds 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 in vestmentHouseholds 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 ofstart-up capital and enterprise expansion.




100

Purpose of BorrowingFigure 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


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 20094.50 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 SizeAnalysis 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 KshsAmount received in Kshs

DCA Area District Minimum Maximum Mean Std.Deviation

DCA1 Bomet 15,000 15,000 15,000 0.0Kisii Central 5,000 150,000 67,833 55,661

Nyamira 5,000 40,000 22,667 7,761Nandi North 15,000 15,000 15,000 0.0Trans Nzoia 20,000 160,000 75,125 49,412Bungoma 15,000 300,000 74,142 83,920Lugari 3,000 500,000 90,111 156,506Nakuru 20,000 150,000 105,000 61,373Total 3,000 500,000 62,477 81,309

DCA3 Bomet 5,000 110,000 37,792 34,993Kisii Central 5,000 150,000 42,000 54,845Nandi North 5,000 40,000 23,000 13,509

Trans Nzoia 20,000 250,000 89,500 83,484Bungoma 20,000 140,000 50,000 40,723Lugari 5,000 350,000 88,714 99,280Uasin Gishu 10,000 150,000 43,552 33,463Nakuru 50,000 161,000 102,750 61,076Total 5,000 350,000 58,272 64,175


4.50.2 Success RateTo 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.




103

Table 89: Success rate in DCA 1 and DCA 3DCA Area

District DCA1 DCA3 TotalBomet 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%


In general, this study showed that 95% of all the applications were successful as shown in Figure

35 below.

Figure 34: Loan Success Rate


4.50.3 Reasons for Unsuccessful Loan ApplicationsTo 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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Figure 36: Type of Payment


4.50.5 Loan Repayment PeriodThis 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 3DCA Area N Minimum Maximum Mean Std. DeviationDCA1 50 2 36 12.24 6.096DCA3

90 2 48 14.09 7.971Total 140 2 48 13.43 7.388Source: Baseline Survey, April 2009

4.50.6 Interest RateThere 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. DeviationDCA1 50 3.00 300.00 28.5200 45.04974DCA3 90 1.50 120.00 18.6089 15.75193Total 140 1.50 300.00 22.1486 29.95018





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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 borrowersactive 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


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: I nterest rates (%) charged by type of lender

Type of lender Minimum Maximum MeanStd.

DeviationRelative 12.00 12.00 12.0000 .Money lender 8.00 29.00 17.2727 6.60441Cooperative 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 43.63198Others lenders 4.00 20.00 10.0000 7.11805Commercial bank 1.50 120.00 21.1571 23.58471MFI 4.00 90.00 20.7979 12.62617Total 1.50 300.00 22.1486 29.95018Source: Baseline Survey, April 2009

Table 94: Size and terms of loans in DCA 1 and DCA 3DCA1 DCA3

DistrictPurpose ofborrowing

Paymentperiod(months)

Interestrate(p.a)

Purpose ofborrowing

Paymentperiod(months)

Interestrate(p.a)

BometEducation 12.0 4.0% Education 12 24.1%Total 12.0 4.0% Non business 12 27%

Buy land 24 10%Maize planting 12 10%Total 13 22.5%

Kisii Central

Education 14 6.7% Education 12 8%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%

Nandi North

Non business 2 120%Buy land 6 27%Buy cattle 12 14.3%Total 8.8 38%

Nyamira Nonbusiness 10 27.3%Buy cattle 11.5 40.5%Total 11.2 37.9%

Trans Nzoia

Education 11 24% Education 22 11%Nonbusiness 12 8% Buy cattle 12 20%Maizeplanting 12 45.5% Farm inputs 12 17%Dairy 12 16% Total 18.67 13.5%




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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 collateralused

DCA Area Total

DCA1 DCA3None

7 2 9Milk deliveries 4 3 7Title deed 6 4 10Guarantor(s) 15 26 41Household goods 9 15 24Savings/Shares 2 24 26Others (specify) 1 0 1Log book 0 2 2Cattle 6 14 20Total 50 90 140


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 MaturityThe 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 smallholdings. 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. DeviationDCA1 50 5,150 575,000 73,026.34 96,853.473DCA3 90 5,500 392,000 68,372.32 74,680.922

Total 140 5,150 575,000 70,034.47 82,963.100Source: 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

District Minimum Maximum MeanStd.

DeviationBomet 5,500 127,800 42,746.15 39,983.176Kisii Central 5,900 160,615 46,125.83 58,747.294Nyamira 12,000 47,000 27,096.47 8,342.172Nandi North 6,000 46,000 26,360.00 15,506.386Trans Nzoia 24,000 310,000 98,735.71 77,375.663Bungoma 17,220 330,000 73,419.47 78,345.807Lugari 5,150 575,000 102,477.00 132,873.854Uasin Gishu 11,500 180,000 52,546.34 40,239.417Nakuru 22,200 213,000 130,087.50 74,282.308Total 5,150 575,000 70,034.47 82,963.100


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 resourcemanagementproblem Bomet

KisiiCentral Nyamira

NandiNorth



Soil erosion11 7 12 12 10 14 10 27 9 112Deforestation 11 7 11 10 10 10 10 15 10 94

Water pollution 12 6 11 13 11 6 9 10 11 89Market places 12 6 11 11 12 6 11 6 11 86Soak pits 11 7 11 8 11 8 11 2 11 80Cattle dips 11 7 11 9 11 8 11 4 11 84Manure disposal 9 6 11 4 11 8 11 4 11 80Sand harvesting 9 7 11 5 10 8 9 10 11 80Human/wildlifeconflict 9 7 12 5 10 10 9 16 9 87

Air pollution 0 0 0 0 0 0 0 1 0 1 Ants 0 0 0 0 0 0 0 2 0 2Total

95 60 101 83 96 78 91 97 94 795Source: 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





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Figure 41: Severity of NRM Problems


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

4.54.1 Roof MaterialsThis 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

District

Roof material used to construct residence ofhousehold head Total

Thatch TinCorrugated

iron TilesBomet 14 0 81 0 95Kisii Central 1 0 59 0 60Nyamira 5 1 95 0 101Nandi North 3 0 80 0 83Trans Nzoia

1 0 95 0 96Bungoma 0 0 78 0 78Lugari 4 1 84 2 91Uasin Gishu 8 6 82 1 97Nakuru 0 0 92 2 94Total 36 8 746 5 795





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District Material DCA1 DCA3 TotalTotal 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%Corrugatediron 8% 2% 4%Total 100% 100% 100%

Source: Baseline April 2009

Figure 42: Wall Materials used in Constructing Households





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4.54.3 Floor MaterialsThe 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 headSource: Baseline Survey, April 2009

4.54.4 Window materials in useThe study showed that 48% of the windowmaterials used was glass and 48% waswood, 2.4% was wire mesh and 0.6% wasopen most of which were in Uasin GishuDistrict as shown in Table 103 below.

Table 103: Window material used to

construct residence of household head

District DCA Area Total

BometMaterial DCA1 DCA3Earth 98% 58% 79%Concrete 2% 40% 20%Wood 0% 3% 1%Total 100% 100% 100%

Kisii Central Earth 44% 49% 47%Concrete 53% 49% 51%Wood 3% 2% 2%Total 100% 100% 100%

Nyamira Earth 39% 46% 44%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%

Nakuru Earth 23% 33% 29%Concrete 74% 67% 70%Wood 3% 0% 1%Total 100% 100% 100%




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District

Window material used to construct residence of household head Total

Wire mesh Tin Wood Glass OpenBomet 0 0 73 22 0 95Kisii Central 0 0 35 25 0 60Nyamira 0 0 37 64 0 101Nandi North

0 0 45 38 0 83Trans Nzoia 3 0 62 30 1 96Bungoma 2 1 34 41 0 78Lugari 3 3 40 45 0 91Uasin Gishu 1 2 38 52 4 97Nakuru 10 0 20 64 0 94Total 19 6 384 381 5 795


4.55 Support to Policy and InstitutionsThe 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 inDCA 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.




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.

final sdcp baseline report- june 30, 2009

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