midline impact assesment report for the dairy sector … · 2018. 11. 19. · page 1 june, 2017...

JUNE, 2017

CONSULTANCY SERVICES FOR CONDUCTING A MID-LINE

IMPACT ASSESSMENT OF THE KENYA MARKET TRUST

PROGRAMMES

MIDLINE IMPACT ASSESMENT REPORT FOR THE

DAIRY SECTOR

(FINAL REPORT)

Prepared By:

AFRICAN RESEARCH AND ECONOMIC DEVELOPMENT CONSULTANTS LTD (AFREDEC)

House 6, Plot LR No. 1870/366/6, General Mathenge Drive, Off Parklands Ring Road

P.O Box 32499- 00600, Nairobi Tel: +254 721650460/0721782526

Email: [email protected]; [email protected] Website: www.afredec.com

mailto:[email protected]

mailto:[email protected]

http://www.afredec.com/

Mid-Term Evaluation of the Kenya Market Trust Programme –Dairy Sector

i

TABLE OF CONTENTS

TABLE OF CONTENTS ........................................................................................... I

LIST OF TABLES ................................................................................................... IV

LIST OF FIGURES .................................................................................................... V

LIST OF ABBREVIATIONS AND ACRONYMS .................................................. VI

1 EXECUTIVE SUMMARY .................................................................. VII

1 CHAPTER 1: INTRODUCTION .......................................................................... 1

1.1 Background Information ...................................................................................................1

1.2 Program Objectives ...........................................................................................................1

1.3 Program Components .......................................................................................................1

1.4 Purpose and Objectives of the Midline Evaluation ........................................................3

2 CHAPTER 2: METHODOLOGY AND APPROACHES..................... 4

2.1 Evaluation Design ...............................................................................................................4

2.2 Sampling Design .................................................................................................................5

2.2.1 Sampling for Dairy Farmers........................................................................................................................... 5

2.2.2 Sampling for the Key Informants ................................................................................................................. 7

2.3 Data Collection Methods...................................................................................................7

2.3.1 Document Review of Secondary Data Collected .................................................................................... 8

2.3.2 Primary Data Collection ................................................................................................................................ 8

2.4 Data Entry and Analysis ....................................................................................................8

2.5 Limitations of the Evaluation............................................................................................9

3 CHAPTER 3: RESULTS AND FINDINGS ........................................ 10

3.1 Introduction ..................................................................................................................... 10

3.1 Dairy Hub’s Membership................................................................................................ 10

3.2 Age of Respondents......................................................................................................... 12

3.3 Dairy Farmers’ Socio-economic and Demographic Characteristics ....................... 12

3.4 Dairy Farming Experience ............................................................................................. 14

3.5 Dairy Farming Systems .................................................................................................. 14

3.6 Herd characteristics and Dynamics .............................................................................. 15

3.7 Results for Supply Chain Component .......................................................................... 18

3.7.1 Target Group Change Level for the Supply Chain Component ........................................................ 18

3.7.1.1 Milk Production ............................................................................................................................................... 18


ii

3.7.1.2 Milk Marketing Channels .............................................................................................................................. 19 3.7.1.3 Volumes of Milk sold ..................................................................................................................................... 21

3.7.2 Dairy Production Costs ............................................................................................................................... 23

3.7.3 Revenues and Gross Margins from Milk production ............................................................................ 25

3.7.3.1 Farmers Access to Information and Inputs .............................................................................................. 26 3.7.3.2 Frequency and Consistency of Information Access ............................................................................... 28 3.7.3.3 Level of Satisfaction with Sources of Information .................................................................................. 28 3.7.3.4 Level of Satisfaction with Supply Chain Services .................................................................................... 29 3.7.3.5 Level of Satisfaction with Inputs Provision ............................................................................................... 30

3.7.4 Market Systems Change Level for the Supply Chain Component .................................................... 30

3.8 Results for Improving and Promoting AI Service Component ................................ 32

3.8.1 Target Group Change Level for Improving and Promoting AI Service Component ................... 32

3.8.1.1 Awareness of Accredited AI Services ....................................................................................................... 32 3.8.1.2 Usage of Accredited AI Services ................................................................................................................ 33 3.8.1.3 Cost of Accredited AI Services .................................................................................................................. 33 3.8.1.4 Level of Satisfaction with Accredited AI Services .................................................................................. 34 3.8.1.5 Perceived Benefits of using Accredited AI Services............................................................................... 35

3.8.2 Market System Change Level for Improving and Promoting AI Service Component .................. 35

3.8.2.1 Success Rate of AI Services.......................................................................................................................... 35 3.8.2.2 AI Service Providers with Improved Customer Service and Business Skills and Investing in

Promotional and Marketing Activities with Relevant Market Actors ............................................... 36

3.9 Results for Commercial Hay Production ..................................................................... 36

3.9.1 Background of Commercial Hay Production Intervention ................................................................. 36

3.9.2 Target Group Change Level for Commercial Hay Production Component .................................. 37

3.9.2.1 Awareness of Quality Assured Hay ........................................................................................................... 37 3.9.2.2 Utilization of Quality Assured Hay ............................................................................................................ 37 3.9.2.3 Perception on the Quality of the Hay Purchased .................................................................................. 38 3.9.2.4 Types of Hay Purchased ............................................................................................................................... 38 3.9.2.5 Sources of Hay ................................................................................................................................................ 39 3.9.2.6 Level of Satisfaction with Quality Assured Hay ...................................................................................... 40 3.9.2.7 Perceived Benefits of Quality Assured Hay ............................................................................................. 41

3.9.3 Market Systems Change Level for Commercial Hay Production Component .............................. 41

3.10 Results on Program’s Impact on the Farmers/Target Beneficiaries ....................... 41

3.10.1 Most Significant Changes to Dairy Farming ............................................................................................ 41

3.10.2 Contribution of Dairy Hubs/Co-operatives to Most Significant Changes to Dairy Farming ..... 42

3.10.3 Resilience Analysis ......................................................................................................................................... 43

4 CHAPTER 4 ACHIEVEMENTS OF PROGRAM INDICATORS...... 45

5 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ....... 55

5.1 Summary of Findings and Conclusions ........................................................................ 55

5.2 Recommendations........................................................................................................... 60

6 APPENDICES ..................................................................................... 62

6.1 Appendix 1: Program Indicators ................................................................................... 62

6.2 Appendix 2: Data Collection Tools ............................................................................... 62

6.3 Appendix 3: DFID’s Livelihood sustainability framework ......................................... 62


iii

6.4 Appendix 3: List of Key Informants Interviewed ........................................................ 63

6.5 Appendix 3: List of Documents Reviewed ................................................................... 64

6.6 Appendix 4: Characteristics of the Treatment Dairy Hubs ...................................... 65

6.7 Appendix 5: Characteristics of the Control Group Dairy Hubs ............................... 71

6.8 Appendix 6: Characteristics of the Treatment Agro Vet Service Providers ......... 74

6.9 Appendix 7: Characteristics of the Treatment AI Service Providers ..................... 76

6.10 Appendix 8: Characteristics of the Commercial Hay Providers .............................. 78

6.11 Appendix 9: Characteristics of the BDS Providers .................................................... 80

6.12 Appendix 10: AAER Model for Supply Chain Intervention ....................................... 82

6.13 Appendix 11: AAER Model for Artificial Insemination Services Intervention ....... 84

6.14 Appendix 12: AAER Model for Commercial Hay Production Intervention ........... 85


iv

LIST OF TABLES

Figure 1.1: Strategic Framework for the Dairy Sector ...................................................................... 2

Table 2.2: Sample per selected Dairy Hub ........................................................................................... 6

Table 2.3: Sample Size for Key Informants .......................................................................................... 7

Table 3.1: Membership of Dairy Hubs ................................................................................................ 10

Table 3.2: Distribution of Membership Sampled by size ................................................................. 11

Table 3.3: Dairy Program Outreach .................................................................................................... 11

Table 3.4: Household Demographic Characteristics ....................................................................... 12

Table 3.5: Mean Household Size and gender distribution .............................................................. 13

Table 3.6: Number of Members in a Household in Different Age Category by Gender ........ 14

Table 3.7: Years of experience in Dairy Farming ............................................................................. 14

Figure 3.1: Main grazing system ............................................................................................................ 15

Table 3.8: Herd Characteristics and Dynamics ................................................................................. 17

Table 3.9: Milk Yields per Cow per Day ............................................................................................ 19

Table 3.10: Percentage distribution of quantity of milk sold by type of outlet.......................... 20

Table 3.11: Volume of Milk Sold per Day .......................................................................................... 22

Table 3.12: Average costs of production (variable costs) .............................................................. 24

Table 3.13: Average Milk Revenue for Treatment Group .............................................................. 25

Table 3.14: Average Milk Revenue for Control Group .................................................................. 26

Figure 3.2: Market Information and Participation in Marketing Activities .................................. 26

Table 3.15: Sources of Information ..................................................................................................... 27

Figure 3.3: Consistency and Frequency of information ................................................................... 28

Figure 3.4: Level of satisfaction on information received ............................................................... 29

Table 3.16: Level of Satisfaction with Supply Chain Services ......................................................... 30

Table 3.17: Level of Satisfaction with Inputs Provision ................................................................... 30

Table 3.18: Performance of the Intervention Dairy Hubs .............................................................. 31

Figure 3.6: Source of awareness of Accredited AI Services ........................................................... 32

Figure 3.7: Usage of Accredited AI Services ...................................................................................... 33

Table 3.19: Agreement Scale on Satisfaction with AI Services (Treatment) .............................. 34

Table 3.20: Agreement Scale on Satisfaction with AI Services (Control) ................................... 34

Table 3.21: Benefits of using Accredited AI Services ....................................................................... 35

Table 3.22: Success Rate of AI Services .............................................................................................. 35

Table 3.23: Trends in Provision of AI Services (No. of customers)............................................. 36

Table 3.24: Hay Production ................................................................................................................... 37

Table 3.25: Proportion of Farmers that Purchase Quality Assured Hay .................................... 38

Table 3.26: Quality of Hay Purchased by Season ............................................................................. 38

Figure 3.8: Types of Hay Purchased .................................................................................................... 39

Table 3.27: Sources of Quality Assured Hay ..................................................................................... 39

Table 3.28: Agreement Scale on Satisfaction with Hay ................................................................... 40

Table 3.29: Benefits of using Quality Assured Hay from farmers’ perception .......................... 41

Figure 3.10: Most Significant Changes in Dairy Farming ................................................................. 42

Figure 3.11: Role of Dairy Hubs in Most Significant Changes ........................................................ 42


Table 3.30: Social and Economic Benefits .......................................................................................... 44

Table 4.1: Summary of Program Achievements per Indicators ..................................................... 47


v

LIST OF FIGURES

Figure 1.1: Strategic Framework for the Dairy Sector ...................................................................... 2

Figure 3.1: Main grazing system ............................................................................................................ 15

Figure 3.2: Market Information and Participation in Marketing Activities .................................. 26

Figure 3.3: Consistency and Frequency of information ................................................................... 28

Figure 3.4: Level of satisfaction on information received ............................................................... 29

Figure 3.6: Source of awareness of Accredited AI Services ........................................................... 32

Figure 3.7: Usage of Accredited AI Services ...................................................................................... 33

Figure 3.8: Types of Hay Purchased .................................................................................................... 39

Figure 3.10: Most Significant Changes in Dairy Farming ................................................................. 42




vi

LIST OF ABBREVIATIONS AND ACRONYMS

AAER Adopt, Adapt, Expand and Respond

A I Artificial Insemination

ASL Associated Steel Limited

BDS Business Development Services

CPD Continuous Professional Development

DH Dairy Hub

DTA Dairy Traders Association

ICT Information Communication Technology

ILO International labour Organization

KCC Kenya Co-operative Creameries

KMAP Kenya Market Assistance Program

KMT Kenya Markets Trust

KIIs Key Informant Interviews

Ksh Kenya Shillings

LGSEA Livestock Genetics Society of Eastern Africa

LOL Land O’ Lakes

M4P Markets Work for the Poor

MAP Market Assistance Program

M&E Monitoring and Evaluation

SACCO Savings and Credit Cooperative Organization

SME Small and Medium Enterprises

SP Service Provider

TNS TechnoServe

UK United Kingdom

USIU United States International University


vii

1 EXECUTIVE SUMMARY

Introduction

Kenya Market Trust (KMT) in collaboration with TechnoServe (TNS) has been

implementing a Market Assistance Program (MAP) in the dairy sector since 2012. MAP

works in partnership with the private sector and government to transform how dairy sector

markets for increased competitiveness inclusive wealth creation for all players. KMT and

TNS use the "Making Markets Work for the Poor (M4P)" approach. This approach lays

emphasis on facilitating the realignment of the incentives, capacities, relationships and rules

which govern how markets work. The program activities were expected to trigger actions

by market actors like dairy hubs, milk traders and other service providers in the dairy

sector, and therefore result in desired systemic changes. Farmers would therefore be able

to better perform and get better returns operating within an improved system. The

programs interventions are in improving milk supply chain management and structures;

addressing issues of feed quality and supply; and improving animal health and genetics

services.

The overall purpose of the midline assessment is to measure and report the progress of the

KMT MAP’s dairy sector interventions performance at midline against the set targets over

the program period, and also use the findings from the evaluations to inform and improve

sector intervention strategies. More specifically the evaluation is to measure the impacts,

outcomes, outputs, validate evaluation framework, make recommendations and inform

logical framework targets.

Methodology

The midline evaluation used a quasi-experimental design that incorporated treatment group

as well as a control group. The study did not adopt the longitudinal approach proposed at

baseline as the population of interest had significantly changed. The changes included none

participation of some of the selected dairy hubs at the baseline, dropping off of some

participants midway, addition of new participants and delays in implementing some of the

program activities. The respondents comprised dairy farmers, dairy hubs, milk processors,

private based agro-vets, A.I providers, milk traders, commercial hay producers and feed

millers that were participating in the program as at March 2016, and comparison enterprises

not part of the program.

Both primary and secondary data were collected using qualitative and quantitative tools. A

household survey was conducted with 473 dairy farming households in twelve (12)

intervention dairy hubs as well 206 individual dairy farming households in five (5) non-

intervention/control hubs. Key Informant Interview (KII) schedules were also developed to

collect qualitative data from selected informants such dairy hub managers, milk processors’

managers, agrovets, A.I providers, milk traders, commercial hay producers, feed millers and

business development service (BDS) providers. Secondary data was collected in the review

of various project documents, program M&E data and other relevant literature. Midline is

compared with data from both the KMT dairy sector baseline study and the dip stick (early

impact assessment in 2014)


viii

Summary of Findings

The three years of implementation of the KMAP program 56,927 farmers from KMT

Monitoring and over 85,000 farmers as estimated by this evaluation benefitted from the

program with a wide range of impacts.

At the midline, the average yield per cow per day was 8.9 litres for the two seasons within

the treatment groups, against 8.4 litres for the control groups and 7.02 liters for the dip

stick survey (2014)

The average number of dairy cows per household for the treatment group was 3 and 2 for

the control group

For treatment group, gross revenue for milk sales per cow per day averaged Ksh. 334.2

during the wet season and Ksh 286.1, net revenues of Kshs. 196.5 during the wet season

and Kshs.148.4 during the dry season with overall cost of production at Kshs. 137.7 per

cow per day or Kshs.15.5 per litre

In comparison, net revenues per cow per day for control group averaged Kshs. 123.9 during

the wet season and Kshs. 98.7 during the dry season with overall cost of production

kshs141.6 per cow per day or Kshs 16.9 per litre.

This increase in incomes enhanced the farmers’ resilience to climate change; farmers

reported that they were able to invest more in their social, natural, human, physical and

financial livelihood assets/capitals according to the DFID’s Livelihood sustainability

framework. Perception of benefits accruing from dairy farming was measured for each of the

capitals using a likert scale of 1 to 5, where 1 represents least benefited and 5 represents

most benefited and both treatment and control groups scored above average across the six

livelihood assets 3.3 and 3.0 respectively.

In terms of achievement of targets set for the program indicators, there was an over 50% achievement for all indicators except those measuring the number of DHs and processors

investing in improved customer-centric distribution and retail strategies; new product

innovations and/or marketing strategies; and improved supply chain management practices.

Supply Chain Interventions

In the supply chain component, as a result of adoption of new innovations promoted by

KMAP thorough its implementers in the private and public sector such as adoption of

improved cattle and increased use of hay led to the increase in milk yields.

Volumes of milk sold to various outlets increased by 53.3% during the wet season and 35.7%

during the dry season. The increase in volumes marketed was associated both with the

increased yields and access to more reliable markets as well as support services.

Farmers preferred selling their milk through formal channels, mainly dairy hubs as indicated

by 93% of the farmers as compared to 70% during the baseline and above the target of 80%.

This is by virtue that most farmers interviewed were members of some hub and the reason

is dairy hubs offered the farmers integrated services including advance payments, quality

feeds and AI services on check off system and extension services in some cases.


ix

A total of 76% of the beneficiary farmers reported improved access to information and

inputs while 70% reported improved supply chain services, all these attributed to improved

distribution and retail channels. It was however, noted that the farmers considered the

prices of milk lower while that of inputs such as AI and Hay considerably high. Notably,

these concerns were not limited to farmers in the project implementation areas but can be

generalized to farmers in the whole country.

Supply Chain - Market actor changes

Five (5) of the intervention dairy hubs and 2 processors (63.6%) had adopted ICT based

systems to improve efficiency in operations. Three (3) ICT firms had developed hotlines,

authentication and rating platforms, and that 12 agrovets were already using such platforms

Five dairy (5) hubs and 2 processors, (KCC and Lattana) were active in investing in

improved customer-centric distribution and retail strategies and in improved supply chain

management practices, and another five (5) dairy hubs “crowded in”

As a result, dairy hubs exposed to professional hub management services continued to

improve their business operations and thus improving the performance. Overall nine (9) of

the intervention dairy hubs had membership numbers growing by 8% to 317%. The quantity

of milk handled also increased in seven (7) of the dairy hubs by 11% to 94%. Eventually,

annual turnovers had increased by between 2% and 100% in eight (8) of the dairy hubs.

Artificial Insemination

Usage of accredited A.I services has more than doubled from 35.3% at baseline to 72.7% at

the midline, thus exceeding the target set by 18%. Farmers that were strongly satisfied with

quality, availability and trustworthiness were 27.8%, 31.2% and 32.6% respectively as

compared to 15%, 19% and 19% respectively at the baseline.

According to farmers’ perceptions, benefits from use of accredited A.I services included

increased milk production for offsprings (77.5%); healthier calves (75.7%); high conception rates (68.6%) and increased incomes (63.2%).

The average price of A.I services was Ksh 1,578 against a set target of Ksh.1, 316. It was

however, noted that farmers were using more exotic semen including sexed semen which

was more expensive compared to the local semen.

These efforts bore fruit because Accredited AI service providers through KMAP’s

engagements with LGSA reported increased AI services provision by 61% between 2013 and

2016, implying an increase in number of farmers adopting A.I services.

Commercial Hay Production

Awareness of farmers on quality assured hay has increased from 9.1% at baseline to 41% by

midline survey. On average 77% (24,083) 1 of the beneficiary farmers accessed quality

assured hay during both the wet and dry seasons with 80% indicating they would be willing

to purchase hay as long as the quality is assured. According to the farmers’ perceptions, the

main benefits from the use of quality assured hay include: increased milk production (85.1%);

1 Exceeded the target set by 71%


x

increased incomes (67.2%); healthier cows (66.7%) and less variation in milk production

(42.5%).

Two (2) commercial hay producers (Hay N Forage Company and Sochon) have shown the

viability of commercial hay farming. Scale up activities include New KCC promoting linkages

between smallholder farmers and commercial hay producers while the County

Governments of Laikipia and Narok had intentions to finance commercial hay production.

Recommendations

To further improve the implementation of the program, the following is recommended:

a) The dairy hubs are the most important source of inputs and marketing channels for

the farmers. In view of this, it is important for KMAP to continue empowering the

dairy hubs to be viable business entities which would be instrumental in propelling

smallholder farmers from poverty;

b) The use of key service providers for continuous professional development of dairy

hubs, milk traders, agrovets and other actors was noted as a good approach through

which challenges in supply chain management as well as delivery of A.I and animal

health services can be addressed. KMAP should therefore promote this approach to

the wider stakeholders in the dairy industry as a best practice and for wider impacts;

c) Benchmarking should be enhanced for dairy hubs and market actors that have

adopted and are implementing the program interventions well so that other actors

may learn from them. This may be done by developing case studies on the successful

dairy enterprises and organizing other dairy enterprises to learn from them.

d) The main challenges experienced by the AI providers are inadequate supplies, lack of

specialized semen handling facilities and equipment’s and facilities as well as

perception by farmers that AI services are expensive; and therefore slow uptake of

A.I in some areas. There is therefore need to provide assistance to the AI service

providers to acquire semen handling facilities and equipment as well as offering more

training and information on modern A.I services to dairy farmers focusing on the

value proposition for use of A.I services; e) KMAP in association with its partners should make efforts in enhancing appropriate

partnerships between the DHs, processors and private sector players as well as the

County Governments so as to improve on supply chain management, marketing and

retail strategies, improved customer-centric distribution and retail strategies as well

as scaling up use of ICT to improve on the services provided to farmers;

f) To ensure sustainability of the program interventions there should be concerted

efforts to involve the County Governments more intensely in the implementation of

the KMAP program. This is because implementation of livestock programs is a

devolved function;

g) In order to effectively inform the program’s theory of change, it is recommended

that for future evaluations, the same methodology applied in the midline assessment

should be applied in the end-line assessment to enhance comparability while the

findings from the midline assessment should be the basis for the setting the targets

for the end-line evaluation.


1

1 CHAPTER 1: INTRODUCTION

1.1 Background Information

As at the end of 2016, Kenya Market Trust (KMT) in collaboration with TechnoServe (TNS)

has been implementing a Market Access Program (MAP) in the dairy sector since 2012.

Initially, SNV Netherlands and TechnoServe both had interventions to implement within the

dairy sector before -the latter- was awarded implementation of the entire dairy work while

SNV focused on water. The initial phase of the program ran from 2012 to 2015.

Technoserve’s engagement included three key components: Improving milk Supply chain

management and structures; addressing issues of feed quality and supply; and improving

Animal health and genetics services. There were however, delays in the initial stages of the program activities with only the feed component starting in 2012 though very slowly due to

contracting issues between KMT and TNS, while the Artificial Insemination (AI) component

started in May 2013. Activities within the supply chain component started informally in 2014

but formalized in 2015.

1.2 Program Objectives

The MAP program was designed to work in partnership with the private sector and

government to transform how dairy sector markets work so that they could become more

inclusive and more competitive. To achieve these objectives, the programme applied the

"Making Markets Work for the Poor (M4P)" approach. This approach is based on facilitating

the realignment of the incentives, capacities, relationships and rules which govern how

markets work. Further, KMT believes that the program activities trigger actions by other

actors and partners and therefore result to systemic changes that are reflected by the

actions taken by the partners or other stakeholders. This is as reflected within an Adopt,

Adapt, and Expand and Respond (AAER) framework.

1.3 Program Components

The program comprises of three broad components namely:

• Supply chain management and structures;

• Feed quality and supply;

• Animal health and genetics services.

One of MAP’s primary interventions in dairy is catalyzing the improvement of practices and

skills of milk supply chain management. This is done through improving both the formal and

informal milk supply chains. The formal milk supply chain the interventions include: Support

dairy co-operatives/dairy hubs obtain professional management and ICT services: Improve

retail and distribution of feed, breed and vet services through dairy co-operatives/dairy

hubs; Support investments in energy efficiency and optimization programs by dairy

enterprises; Support investments by processors in volume and quality based milk payment

systems. These interventions are expected to lead to increased professionalism in dairy

enterprise management through professional management services, new investors,

turnaround, energy efficiency and integration of better input services, products and

practices and improvements in the distribution and retailing strategies of key dairy inputs

and services through milk aggregation centers.


2

Informal milk supply chain is the flow of milk from the producer to the end consumer

without undergoing any type processing (KDB, 2016). The milk in the informal sector is sold

raw which poses a safety concern. MAP has developed and is facilitating business strategies

to support the sale of safe, affordable milk by traders as well as enabling and increasing

access to quality dairy inputs by the farmers delivering to the informal chain. This

intervention seeks to create incentives to drive formalization from within the market system

through promoting investments by the traders to supply quality safe milk to the market and

from processors to capture informal milk and enhancing growth of interconnected services

(i.e., vet, AI, feed, etc.) to the farmers delivering to the informal sector.

On access to feeds and supplements, the project anticipated increased access to quality and

affordable feeds and supplements. This was to promote high productivity as well as reduce

fluctuations in milk production especially during dry seasons. The interventions in this

component include: Support commercial hay producers to improve production, expand

their business and adopt faster growing fodders (hay); support feed manufacturers to

develop formulation strategies based on quality fodder, concentrates and supplements; and

build the capacity of labs to market and co-brand quality feed.

The project also worked to improve practice in artificial insemination (AI) and animal health

services and ensure greater rural penetration through reduced cost and better

infrastructure. The Dairy sector intervention’s strategy is summarized in the strategic

framework in figure 1.1 below.

DAIRY SECTOR THEORY OF CHANGE

Facilitate change in distribution and retailing strategies of breed,

feed and veterinary services

Facilitate the turn around of underperforming dairy hubs, and support

alternative supply chain investment models

Professional, customer-oriented retail distribution startegies in use

by breed, feed, and veterinary service providers

Dairy hubs and other dairy processors are managed effficiently and have

improved supply chains

Increased number of farmers using improved and certified breed, feed and veterinary

services

Improved quality and quantity of milk supply

Increased demand for quality milk in formal markets

Increase in milk sales in formal milk markets

Increased dairy farmersdisposable income

IMP

AC

TTA

RG

ET G

RO

UP

CH

AN

GE

MA

RK

ETSY

STEM

CH

AN

GE

INTE

RV

ENTI

ON

S

Figure 1.1: Strategic Framework for the Dairy Sector


3

1.4 Purpose and Objectives of the Midline Evaluation

The overall purpose of the midline assessment is to measure and report the progress of the

KMT MAP for the dairy sector at midline against the set targets over the program period,

and also use the findings from the evaluations to inform and improve sector intervention

strategies. Prior to this assessment, KMT had conducted a baseline assessment in 2013 and

an early assessment in 2014 as a preamble to the midline and end-line assessment.

The objectives of the midline assessment are to:

i. Identify and measure impact among programme beneficiaries of the dairy

sector's work through an assessment of the selected market actors;

ii. Measure and validate outcomes among programme beneficiaries of the dairy

sector interventions through this assessment of the selected market actors

and beneficiaries;

iii. Measure and validate outputs among the selected market actors of the dairy

sector interventions through this assessment;

iv. Validate the framework provided from the baseline surveys and propose

improvement if any for future impact assessment/evaluations (end-line

evaluation);

v. Identify lessons learned and make recommendations to inform current KMT

dairy sector strategies, and future (climate change mainstreaming?)

programming in the application of market systems approaches in Kenya and

other countries; and

vi. Inform KMT’s logical framework targets.


4

2 CHAPTER 2: METHODOLOGY AND APPROACHES

2.1 Evaluation Design

At the baseline, the evaluation design for dairy sector was based on a quasi-experimental design

where treatment and control groups were established. This approach allows for statistical

attribution of the causal impacts of the program on outcomes for the participants by collecting

before-and-after measures on outcomes of interest from both the treatment and the

comparison groups (Reichardt C.S, 2009)2.

At the time of conducting the baseline, a total of 15 dairy hubs were sampled as the treatment

groups while 14 dairy hubs were sampled as control groups. At this point, the comparison

group was used to represent the counterfactual i.e., what would have happened to the beneficiaries in the absence of the program. It was however, noted that this approach often

suffers from contamination of the comparison groups3.

The baseline also proposed that the research design for the midline and the end line include

longitudinal survey4 where the same respondents that were sampled at the baseline would be

sampled at the midline. Additionally, the baseline proposed to use of propensity score matching

(PSM) and difference in difference (DID)5 approach to measure the key impacts from the

program. It was however, noted that a lot of changes occurred during the first phase of project

implementation such that some of the targeted businesses during the baseline were no longer

participating in the program while new ones that were not initially targeted may have joined.

Further, some businesses that participated in the initial years have since dropped out for one

reason or another. These dynamics implies that it would be difficult to undertake a longitudinal

survey at the midline as had been envisioned at the baseline.

It is to be noted that although the baseline survey of 2012 proposed that a quasi-experimental

design using a longitudinal based survey for both the midline and the end line surveys (where

midline and end line were to target same respondents as those in the baseline), the

methodology at the midline was slightly adjusted to take care of the many changes that had

occurred between the baseline and midline. These changes included none participation of some

of the selected dairy hubs at the baseline, dropping off of some participants midway, addition of

new participants and delays in implementing some of the program activities. As a result the

study was not able to compare some of the indicator values from the baseline with the midline

values. In addition, some approaches used in the baseline such as the propensity score matching

(PSM) would not be possible at the midline. This necessitated the use of the dip stick (early

impact assessment in 2014) survey data as the baseline values for some of the indicators. The

2 Reichardt C.S, The SAGE handbook of quantitative methods (2009). 3 Ruffer, Tim and Elise Wach (2013) “Review of M4P Evaluation Methods and Approaches” ITAD Report Commissioned by the UK Department for International Development 4 Where the same respondents for the baseline are interviewed at the midline and the end line 5 This measures the net effect of the program interventions by netting changes from the control group from the changes in the treatment group so as to arrive as the “net change”


5

midline also used the difference in difference approach to compare changes over time and

across the treatment and the control group for the key indicators to the extent possible.

The midline evaluation still used a quasi-experimental design as proposed in the baseline. This

design involved a treatment and a control group. The preference of the design was so as to

allow the measurement of changes by comparing the control and the treatment groups as well

as over the implementation period. However, after noting the various changes that have

occurred between the baseline in 2012 and the midline in 20166, the methodology for the

midline disregarded the use of same respondents as those used in the baseline as well as the

use of PSM. In addition, the use of DID was applied to assess the various indicators only to the

extent possible. The overall indicators that were assessed are presented under Annex 1. The

midline also adopted the 2014 dipstick survey to fill in gaps for information that could not be

found in the baseline report of 2012. Further, the midline evaluation made a few additions

including a re-definition of the target respondents (either as the treatment or the control

group). For the purposes of the midline evaluation, the treatment group was defined as those

businesses that were still participating in the program as at March 2016. These businesses

included dairy farmers, dairy hubs, milk processors; private based agro-vets, A.I providers, milk

traders, commercial hay producers and feed millers.

2.2 Sampling Design

2.2.1 Sampling for Dairy Farmers

The midline evaluation sampled individual farmers/households from 14 out of 26 participating

dairy hubs for the treatment group and 6 control dairy hubs that were selected in consultation

with the client7. The selection of the dairy hubs to be interviewed was based on the following

criteria:

(i) Target components - Depending on the interventions that the program worked with.

These include milk value chain, feeds and A.I & genetics (1st Strata).

(ii) Regional distribution - Key regions include Central, Eastern and Rift valley (2nd Strata).

(iii) Random selection of dairy hubs.

(iv) Listing of active members of the dairy hubs stratified according to the milk collection

centres.

(v) Random selection of farmers from the list of farmers.

The study selected 14 treatment dairy hubs and 6 control dairy hubs8, totaling to 20 dairy hubs.

In each dairy hub selected, the numbers of milk collection centers per route were determined

with assistance from the dairy hub managers. After this, dairy farmers were randomly selected

from each of the milk collection centers.

6 KMT 2016 Dairy impact assessment inception report 7 The sampling was done through the guidance of KMT M&E Team and Technoserve 8 The consultant had proposed 14 control hubs but it was difficult getting all the 14 dairy hubs due to

contamination.


6

The sample size was calculated using the Yamane (1967:886) formula as below.

n =N/1+N (e)2

Where:

n is the sample size,

N is the population size. For this study, N was 91,865 (This was the number of farmer

beneficiaries of MAP as at March, 2016)9

e is the desired level of precision (For this study, we will use ±4%)

n=91,865/1+91,865(0.04)2= 807 farmers

This yielded a total sample size of 807 dairy farmers. This sample size was shared equally for

each dairy hub. This implied that for each dairy hub, 40 farmers were randomly selected to

participate in the study, totaling to 800 farmers (560 for the treatment group and 240 for the

control group).

However, the achieved sample size was 679 dairy farmers (473 for the treatment group and

206 for the control group). The main reason for the non-achievement of the proposed sample

of 800 farmers was because some of the dairy hubs such as Ndumberi and Kikuyu dairy

cooperatives were not willing to participate in the study10. In addition, Gatundu Dairy farmers’

cooperative was no longer in existence11. The achieved sample size per dairy hub is as

illustrated in Table 2.2 below.

Table 2.2: Sample per selected Dairy Hub

Comp

onent

Region Selected

Treatment

dairy hubs

Propos

ed

sample

size

Achieve

d

sample

size

Recommende

d Control

dairy hub

Propose

d

sample

size

Achieve

d

sample

size

Milk

supply

chain

Central Limuru 40 33 Kabete 40 41

Gatamaiyu 40 40 Kikuyu12 40 0

Gatundu

South13

40 0

Eastern Mkulima

Bora

40 40

Tumaini 40 40

Rugendo 40 40

Rift valley Torongo 40 39 Olbutyo 40 42

Ololunga 40 40

A.I & Central Nyala 40 39 Umoja 40 43

9 Source: Kenya Market Assistance Programme project closure report: Jan 2012 - Mar 2016 10 Ndumberi Dairy Co-operative Society and Kikuyu Dairy Co-operative Society 11 Gatundu Dairy Farmers Co-operative Society seized operations and did not participate in the program 12 Dairy hub not willing to participate 13 Dairy hub no longer exists


7

Comp

onent

Region Selected

Treatment

dairy hubs

Propos

ed

sample

size

Achieve

d

sample

size

Recommende

d Control

dairy hub

Propose

d

sample

size

Achieve

d

sample

size

Geneti

cs

Njabini 40 40 Nandrasi 40 41

Karati 40 40 Kahuru 40 39

Eastern -

Rift valley Siongiroi 40 42

Feeds Central Nyala Same as

above

Same as

above

Ndumberi14 40 0

Eastern Meru

Multipurpose

40 40

Rift valley -

Total 560 473 240 206

2.2.2 Sampling for the Key Informants

Key informants for this study were identified purposively based on the roles they played in the

program All the hubs that had been identified for dairy farmer sampling were enlisted for Key

Informant Interviews to understand the performance changes in those hubs which lay platform

for improved farmer performances. See summary in table 2.3 below.

Table 2.3: Sample Size for Key Informants

Category of respondents Targeted sample size Achieved sample size

Hub officials/ board members 21 16

Agro-vets 5 10

Independent A.I providers 21 10

Informal Milk buyers/ traders 2 2

Milk processors 4 2

Commercial hay producers 4 3

Feed millers 1 1

Hay input suppliers 1 0

Management firms 6 4

Total 65 51 Note: The names and contacts of organizations interviewed are in appendix 2

2.3 Data Collection Methods

Qualitative and quantitative evaluation techniques were employed to generate as much

information as necessary for assessing project performance as well as drawing lessons from the

project implementation. The main methods used were: document review; in-depth Key

Informant Interviews (KIIs); and household surveys.

14 Dairy hub not willing to participate


8

2.3.1 Document Review of Secondary Data Collected

Secondary data collection included a desk review of various project reports, M&E data and

other relevant literature. Literature reviewed provided some of the necessary information

required in achieving the objectives of the evaluation such as key monitoring data. Literature

reviews also allowed for identification of information gaps which was then incorporated in the

data collection tools developed. The findings of the desk review were triangulated with the

primary data. The list of documents reviewed is presented in Appendix 3.

2.3.2 Primary Data Collection

Primary data was collected using both quantitative and qualitative techniques depending on the

target respondents. Quantitative techniques using household survey questionnaires were used

to collect data from dairy farmers while qualitative techniques using key informant interviews

(KIIs) guides were used to collect in-depth data from key informants summarized in table 2.2

and appendix 2.

Prior to data collection, enumerators and supervisors were taken through a comprehensive

two-day training covering all aspects of data collection including a question-by-question review

of the data collection tools. Thereafter, a pre-test was conducted for the farmer questionnaire

in Gatamaiyu Dairy Co-operative, where 10 farmers were interviewed. The purpose of the pre-

test was to ensure that the tools were able to effectively capture the required data to meet the

objectives of the evaluation. To ensure data quality, each region had a supervisor overseeing the

data collection. Supervisors also conducted call backs, back checks and spot checks.

2.4 Data Entry and Analysis

Primary quantitative data was entered into Statistical Packages for Social Sciences (SPSS)

software by qualified data entry clerks. The data was cleaned to check for missing values,

incorrectly entered data, inconsistent data and outliers. The data was cleaned by running

frequencies on all variables in the dataset. From the frequency information “missing data” and

the extent to which data is missing was identified. Variables that have been entered incorrectly

were also identified by looking at the “value labels”. Measures of central tendency (mean) and

measures of dispersion (standard deviation) were also compared. Measures of distribution

(skewness and kurtosis) as well as visual inspection were used to show whether the data was

normally distributed.

Quantitative data was then analyzed to generate the required statistics using different methods

such as frequencies, descriptive statistics, cross tabulations and multiple responses analysis,

amongst others. Where necessary, data was re-coded and new variables generated, split or

merged to arrive at the required results. Analyzing the secondary and qualitative data involved

content analysis by reading through the interview or focus group transcripts and other data,

developing codes, coding the data, and drawing connections between discrete pieces of data.

We went through all the key informant interviews with an eye for common themes, categories,

and patterns.


9

To improve the quality (validity and reliability) of data, triangulation (comparison of data with

other sources), was applied as necessary.

2.5 Limitations of the Evaluation

Major limitations for the midline survey included the following:

i. The nature of the KMAP approach involves players willing to work with the program

during its implementation. It was noted that for some of them, engagement naturally

came to an end as the intended purpose was achieved while others dropped out of the

interventions prematurely. This meant that some of these respondents could not be

reached as there were no longer working with the program. To address this, the

midline study had to redefine the treatment and the control groups at the midline.

ii. It was also noted that some treatment groups were new entrants who came on board in

the later years of program implementation. While adaptation and some response started

happening in line with the KMT’s systemic change model, a baseline was not conducted

for the new baseline. This meant that comparison between the baseline values and the

midline values became difficult and therefore the use of quasi-experimental design

(QED) as envisaged in the baseline study was limited. Similarly, the use of the difference

in difference approach was only limited to indicators whose baseline values were

available. To address this challenge, the study approach was adjusted during the inception phase of the midline survey in consultation with KMT and TNS to enable

capture the real impacts of the program. For example, the dip stick survey of 2014 was

to be used in some instances as the baseline value. It is further recommended that for

future evaluations, the same methodology applied in the midline assessment should be

applied in the end-line assessment to enhance comparability while the findings from the

midline assessment should be the basis for the setting the targets for the end-line

evaluation.

iii. There was reluctance from some of the targeted respondents to participate in the

midline study. This not only meant that the study was unable to meet the expected

sample size but also resulted to incomplete information. To address this, the study

relied on secondary data mainly from KMT and TNS where information was not

available from primary respondents.


10

3 CHAPTER 3: RESULTS AND FINDINGS

3.1 Introduction

This chapter presents the key findings of the midline impact assessment study. The first section

describes the dairy farmers’ demographic and household characteristics, dairy hub

characteristics and dairy farming dynamics. This is then followed by the key findings on the

program components namely: supply chain; improving and promoting A.I service; and

commercial hay production. This is discussed along the program aspects of target group change,

market systems change and program impacts.

3.1 Dairy Hub’s Membership

The membership of the dairy hubs involved in the study is summarized in Table 3.1 below.

From the findings, it was evident that all the treatment dairy hubs, (except Tumaini which had

retained the same membership), exhibited a growth in membership ranging from 8% to 317%

between the years 2013-2015. The control dairy hubs also exhibited a 4% to 100% growth in

the number of members during the same period.

Table 3.1: Membership of Dairy Hubs

Treatment Dairy

Hubs

Year % growt

h 2013-2015

Control Dairy

Hubs

Year % growt

h 2013-2015

2013 2014 2015 2016 2013 2014 2015 2016

Nyala 10,000 10,000 11,963 12,671 27% Kabete 2,268 2,311 2,364 2,367 4%

Njabini 0 500 610 680 36% Olbutyo 0 0 400 1384 59%

Karati 500 700 750 720 44% Umoja 495 526 692 756 40%

Torongo 2,800 2,845 2,892 3,037 8% Nandaras

i

110 120 130 134 18%

Siongiroi 9,690 10,350 11,228 12,215 26% Kahuru 62 124 124 124 100%

Tumaini15

160 160 160 0%

Limuru 9,000 9,500 9,800 10,365 15%

Mkulima16 Bora

146 250 625 644 341%

Rugendo17 0 0 0 208

Gatamaiyu 200 300 350 400 100%

Olololung’a 130 80 120 140 8% Total 32,46

6 34,68

5 38,49

8 41,24

0 61%

2,935

3,081

3,710

4,765

Source: Dairy hubs’ managers’ interviews

15 Tumaini started operations in 2014

16 Was registered in 2012 but started operations in 2013 17 Rugendo started operations in 2008 (Rugendo dairy alliance as a community project) collected upto 2014 then

stopped. In 2015 it was transformed into a cooperative but only resumed milk collection operations in Feb 2016. Membership records between 2008 and 2014 are therefore missing.


11

Dairy hubs exhibited different numbers of membership based on the years they have been in

operation where older hubs had higher membership numbers. Based on membership, the hubs

were divided into 3 categories namely: small, medium and large as in Table 3.3 below

Table 3.2: Distribution of Membership Sampled by size

Size of the Hub Name of the Hub Membership Average membership Proportion

Large Hubs Nyala 12,671 9,572 36%

Singiroi 12,215

Limuru 10,365

Torongo 3,037

Medium Hubs Karati 720 675 27%

Njabini 680

Mkulima Bora 625

Small Hubs Gatamayu 300 202 36%

Rugendo 208

Tumaini 160

Ololulunga 140

Using the distribution of the hubs by size and percentage share, and extrapolating from the

KMT data indicating a total of 23 organizations18 that participated in the dairy sector

interventions, this translates to a total of 85,980 members as in Table 3.3.

Table 3.3: Dairy Program Outreach

Size of the Hub Proportion Number of

organizations

Average Membership Total

Membership

Large Hubs 36% 8 9,572 80,057

Medium Hubs 27% 6 675 4,234

Small Hubs 36% 8 202 1,689

Total

85,980

Using this approach, the impact outreach numbers are much higher than the KMT reported

numbers 56,927 beneficiaries by the end of March 201619.

Notes:

(i) *Meru Multipurpose beneficiaries could not be well captured since the cooperative is made up of member

hubs

(ii) *Feed component was implemented in Nyala and Meru Multipurpose dairy hubs (iii) Improvement and promotion of AI services was implemented in Nyala, Njabini, Karati and Siongiroi hubs (iv) Formal supply chain interventions were implemented in Limuru, Gatamaiyu, Mkulima bora, Tumaini, Rugendo,

Torongo and Ololulung’a dairy hubs (v) Targeted Informal supply chain dairy hubs were Kabete, Kahuru, Nandarasi, Olbutyo and Umoja dairy hubs (vi) Numbers include households served through the informal supply chain

18 Includes dairy hubs, commercial hay and forage producers, County governments and processors 19 Source: KMT Dairy sector tracker 2016.


12

3.2 Age of Respondents

The average age of the respondents was 46 years with a minimum of 18 and a maximum of 88

years. Out of the total number of respondents, 164 (24.4%) were aged between 18 and 35

years (youth), 342 (50.4%) were aged between 36 and 55 years while another 24.7% were aged

above 56 years.

3.3 Dairy Farmers’ Socio-economic and Demographic Characteristics

This sub-section illustrates the socio-demographic characteristics of the dairy farmers that

participated in the evaluation based on aspects such as sex, marital status, education, and employment, sources of income, household size and age. It is important to understand the

demographic and socio-economic characteristics of dairy farming households as these influence

farming decisions, choice and adoption of innovations. The percentage for male-headed

households was higher in the treatment group (66%) compared to the control group (52%) see

Table 3.4.

Table 3.4: Household Demographic Characteristics

Characteristic Treatment Control

N % N %

Sex of the Household Head

Male 311 66 107 52

Female 162 34 99 48

Sub-Total 473 100 206 100

Relationship of respondent with head of household

Head of household 318 67 127 62

Spouse 116 25 63 31

Son 20 4 4 2

Daughter 4 1 9 4

Parent 8 2 2 1

Sibling 4 1 0 0

Employee 3 1 1 1

Sub-Total 473 100 206 100

Marital Status of the respondent

Married 389 82 157 76

Single 52 11 26 13

Separated 4 1 1 1

Divorced 2 0 3 1

Widow/Widower 26 6 19 9

Sub-Total 473 100 206 100

Highest level of Education

None 20 4 14 7

Adult Literacy 30 7 7 3

Some Primary 36 8 34 17

Completed Primary 121 26 53 26

Some Secondary 51 11 20 10

Completed Secondary 132 28 52 25

University 48 10 20 9


13

Characteristic Treatment Control

N % N %

Vocational Training 35 7 6 3

Sub-Total 473 100 206 100

Employment of household head

Farmer 376 80 180 87

Own Business 46 10 11 5

Formal Employment 36 8 9 4

Informal Employment 15 3 6 3

Sub-Total 473 100 206 100

Highest source of income

Production and sales of cereals, pulses and tubers 24 5 15 7

Production and sales of vegetables 28 6 13 6

Production and sales of fruits 9 2 0 0

Sale of milk 300 66 143 71

Production and sales of livestock and other livestock products 23 5 1 1

On farm casual labor (On -Farm kibarua) 2 1 2 1

Off-farm labour (Non-farm kibarua) 3 1 3 2

Salaried labour 42 4 10 5

Business activities 22 5 14 7

Remittance (receiving money from others 1 1 0 0

Sub-Total 454 100 201 100

Source: Dairy farmers’ household survey

The farmers interviewed in the household survey were majorly heads of households (67% for

treatment and 62% for the control). These heads of the households were mostly male and were

the owners of the dairy enterprises. Majority of the respondents were also married (82% for

the treatment, 76% for the control), and had completed primary education levels and above

(81% for treatment, 73% for the control). The level of literacy is a crucial indicator to the

adoption of improved technologies as it enables target farmers to discern the importance of

such technologies. The main occupation for the household heads was farming. Nonetheless, it is

worth noting that the percentage was higher under the control group (87%), compared to the

treatment group (80%). Sale of milk was the highest source of income for majority of the

treatment (66%) and control group farmers (71%). This implies that dairy farming remained a

very important enterprise to the communities in the areas under review.

On average, there were 3 males and 3 females in the household; an average total of 6

household members under the treatment group and 6 members in the control group (Table

3.5).

Table 3.5: Mean Household Size and gender distribution

Treatment Control

N Mean N Mean

Number of male persons in the household 473 3 206 3

Number of female persons in the household 473 3 206 3

Total number of households 473 6 206 6



14

The number and age distribution of household members is often used as an indicator of labour

availability within a household, and therefore a determinant of whether the family would adopt

the technology or not. The most active age group in terms of labour provision would generally

fall between 18 and 64 years. The average number of household members aged between 18 to

64 years in each household was 2 persons for both the treatment and control groups, implying

that in each household, there were two members within the active labour force age group

(Table 3.6).

Table 3.6: Number of Members in a Household in Different Age Category by Gender

Males Females

N Mean N Mean

Treatment

Below 5 years 142 1 113 1

6-17 years 247 2 234 2

18-35 years 245 2 262 1

36-64 years 314 1 279 1

Over 64 years 45 1 42 1

Control

Below 5 years 51 1 49 1

6-17 years 86 1 87 1

18-35 years 107 1 113 1

36-64 years 94 1 99 1

Over 64 years 25 1 25 1


3.4 Dairy Farming Experience

On average, the farmers interviewed had 12 years of experience in dairy farming, for both the

treatment and control group (Table 3.7). Men had more years of experience in dairy farming

compared to their female counterparts averaging 13 years for male and 12 years for female for

both the treatment and control groups. The more experience of males in practicing dairy

farming is attributable to the fact that the dairy enterprise is generally male dominated mainly

because it is considered a capital intensive enterprise, which often women farmers do not have

equal access to.

Table 3.7: Years of experience in Dairy Farming Type of Group Male N Female N Both N

Treatment 13 311 12 162 12 473

Control 13 107 12 99 12 206


3.5 Dairy Farming Systems

The main production systems amongst the treatment group was zero grazing, 69% of the

households kept their animals using this system (Figure 3.1) as compared to 65.8% during the


15

baseline study. For the control group households, semi-zero system was the most popular (42%

percent). This is not unusual given the areas where sampling was done. Majority of dairy

farmers in Kenya practice zero or semi-zero grazing systems for efficient milk production (Bebe

et al., 2003)20.

Figure 3.1: Main grazing system


Free range grazing system was least popular amongst the three grazing systems for both

treatment and control groups. The higher number of farmers practicing zero grazing in the

treatment group could be attributed to adoption of new innovations especially those that were

introduced by KMAP especially on quality feeding. Additionally, the areas sampled are

predominantly high potential areas where dairy production adopts intensive systems. The key

informants confirmed that most farmers fed their dairy cows with hay, silage and Napier grass

either under zero grazing or semi zero grazing.

3.6 Herd characteristics and Dynamics

Table 3.7 gives a detailed description of the herd dynamics in the last one year. Herd sizes

varied from one hub to another with Ololung’a, Torongo and Siongiroi hubs having the highest

herd sizes of 16, 12 and 11 respectively. Amongst the control group, the largest herd size was

in Olbutyo with a total of 8 while Nandarasi and Karuhu hubs had 7 each. The average herd size

was 9 for the treatment group and 7 for the control group.

On average, the dairy farmers within the treatment group had an average of 5 milking cows,

including 3 that were being milked and 2 that were dry as at the time of the survey while those

within the control group had an average of 4 including 2 that were being milked and 2 that

were not being milked as at the time of the survey. In addition, farmers in the treatment group

had an average of 4 calves including 2 male and 2 female. The average number of female calves

20 Bebe, O.B., Udo, H.M.J., Rowlands, G.J., Thorpe, W., 2003 Smallholder dairying systems in the Kenya highlands:

cattle population dynamics under increasing intensification. Livest. Prod. Sci. doi:S0301-6226(03)00013-7


16

within the control hubs was 2 and 1 for the female calves making an average of 3 calves. The

lower number of calves in the control group can be associated with the lower number of

mature cows. It is also an indicator of lower herd growth rates.

The number of cows is an important factor in dairy production; however the breed

composition is even more important. Most farmers both in the treatment and control groups

kept improved breeds of cows, with very few keeping pedigree cows (Table 3.8). Nonetheless,

there were more farmers in the treatment group rearing pedigrees than in the control group.


17

Table 3.8: Herd Characteristics and Dynamics Categ

ory

Herd Characteristics Limu

ru

Gatama

iyu

Mkuli

ma bora

Tumai

ni

Rugen

do

Toron

go

Ololun

ga

Nya

la

Njabi

ni

Kara

ti

Siongi

roi

Meru

Multipurpose

Avera

ge

Treatment

Number of Cows Currently Milked

4 2 2 2 2 5 6 3 2 2 4 2 3

Number of Cows not Currently Milked

2 1 1 1 1 3 3 3 1 2 3 1 2

Number of Female Calves Currently Owned

2 1 1 1 1 3 4 2 2 1 3 1 2

Number of Male Calves Currently Owned

2 1 1 1 1 1 3 1 2 1 2 1 2

Total Average number

of cows

11 6 5 5 5 12 16 9 7 6 11 6 9

Name of dairy Hub Kabe

te

Olbutyo Umoj

a

Nandar

asi

Kahur

u

Avera

ge

Control Number of Cows

Currently Milked

2 2 2 2 3 2

Number of Cows not

Currently Milked

1 2 2 2 2 2

Number of Female Calves

Currently Owned

1 2 1 2 1 2

Number of Male Calves

Currently Owned

1 2 1 1 1 1

Average Herd size/hub 5 8 6 7 7 7



18

3.7 Results for Supply Chain Component

The expected outcome of KMAP’s supply chain intervention is to enhance effectiveness and

efficiency of milk value chain actors for eventual benefit of small holder farmers. This is to be

done through formal and informal supply chain interventions. Formal supply chain interventions

included: supporting dairy co-operatives/dairy hubs obtain professional management and ICT

services: improving retail and distribution of feed breed and vet services through dairy co-

operatives/dairy hubs; supporting investments in energy efficiency and optimization programs by

dairy enterprises; and supporting investments by processors in volume and quality based milk

payment systems.

Informal supply chain interventions include: supporting the development of structures to

support sale of safe, quality and affordable milk by informal milk traders; supporting traders to

improve milk collection and input supply systems and infrastructure: working with equipment

providers to introduce payment plans and credit options for the informal milk traders. For

purposes of discussing the findings of the midline evaluation, formal and informal supply chain

intervention findings will be discussed together for the levels of target group change and market

system change. The impacts for all the components will be discussed at the last section of the

findings.

3.7.1 Target Group Change Level for the Supply Chain Component

3.7.1.1 Milk Production

All the efforts of KMAP project should culminate to increased productivity and efficient

functioning of the milk market markets for the benefit of the smallholder farmers. Table 3.9

shows the milk production under the treatment and control groups in various seasons of the

year. Within the treatment groups, the average yield per cow per day was 9.7 litres during the

wet season and 8.0 litres during the dry season; averaging 8.9 litres for the two seasons. This

was 16.8% short of the targeted 10.7 litres. The average yields per cow per day for all seasons

were higher at the midline at 8.9 litres than at dip stick (early impact assessment) in the year

2014 which was 7.02 litres.

The average number of dairy cows for the treatment groups was 3. This therefore meant that

the daily milk production for a household within the treatment group was 29.1 litres during the

wet season, 24 litres during the dry season and an average of 26.7 litres for the two seasons.

The higher amount of milk produced in the treatment group was largely attributable to the

program interventions such as quality feeding and use of and better management of improved

breeds21.

The average number of dairy cows for a household within the control group was 2 with the

average daily yields being 9.3 litres per cow per day during the wet season and 7.5 litres during

the dry season; averaging 8.4 litres for both seasons. This meant that the total daily milk yields

21 This was reported by the dairy hub managers


19

per household per day was therefore 18.6 litres for the wet season, 15.0 litres for the dry

season and an average of 16.8 litres for both seasons (Table 3.9).

Table 3.9: Milk Yields per Cow per Day Dairy Hub Average Cows

Currently Milked/Farmer

Amt produced

(Wet season/farmer)

Average/

cow/day (wet season)

Amt of Milk

produced (Dry Season/farmer)

Average/

cow/day (dry season)

Average

lts/cow/day (All seasons)

# Treatment

1 Limuru 4 30 7.8 25 6.5 6.9

2 Gatamaiyu 2 18 8.8 16 7.7 8.1

3 Mkulima

bora

2 25 14.3 22 12.8 13.5

4 Tumaini 2 18 11 16 9.6 10.5

5 Rugendo 2 27 14.2 24 12.3 13.5

6 Torongo 5 28 5.7 20 4.1 5.4

7 Ololunga 6 31 5.4 22 3.9 5.3

8 Nyala 3 27 10.3 20 7.9 9.4

9 Njabini 3 27 8.8 24 6.1 7.8

10 Karati 2 23 11.3 16 7.7 8.8

11 Siongiroi 4 25 6.8 17 4.7 5.9

12 Meru Multipurpose

2 27 12.8 27 12.4 12.4

Overall

Averages

3 29.1 9.7 24.1 8.0 8.9

Control

1 Kabete 2 26 13 25 12.5 12.8

2 Olbutyo 2 13 6.5 11 5.5 6.0

3 Umoja 2 22.5 11.3 13 6.5 8.9

4 Nandarasi 2 17 8.5 14 7 7.8

5 Kahuru 3 22 7.3 18 6 6.7

# Overall Averages

2 18.6 9.3 15.0 7.5 8.4


* Outliers were removed from the data

On average, the achieved milk yield was higher than the national averages of approximately 7.1

liters per cow per day22. It should however, be noted that the KMT program intervened in

mostly high potential areas while the national average, includes production includes yields from

low production agro-ecological zones such as the coastal low lands and the Arid and Semi-Arid

areas.

3.7.1.2 Milk Marketing Channels

One of the dairy interventions’ objectives is strengthening farmers’ collective action for them to

be able to access formal markets and be influential actors in the dairy value chain.

22 Kenya dairy Board, 2014


20

Over 80% of milk from the treatment group went to the hubs. The increase in volumes

marketed was associated both with the increased yields and access to more reliable markets as

well as support services. From the midline assessment, 93% of the milk is sold through formal

channels. At baseline 65% of the milk went hubs, 36% to traders/venders and other consumers.

(Table 3.11).

Dairy Co-operative/hub was the most used milk marketing channel23. For the treatment group,

91% of the milk is sold to dairy co-operatives/hubs with venders/traders, direct consumers and

private processors account for 5%, 2% and 2% of the milk sold respectively (Table 3.10). This

implies that 93% of the milk is sold through formal channels (Dairy co-operative and private

processors). This is much higher compared to the baseline where approximately 70% of the

milk was sold through formal channels and 86% for the control group. These results may be

attributed to KMTs intervention of improvement of practices and skills of supply chain

management for the dairy hubs.

It is important to note that the high proportion of farmers selling their milk through formal

channels may be attributed to the fact that the respondents interviewed are affiliated to dairy

hubs/co-operative and they therefore sell most of their milk through those dairy hubs.

However, it is known in Kenya that most of the small scale dairy farmers (about 80%) sell their

milk through informal channels, and majority may not be affiliated to any dairy hub/co-

operative/enterprise. Staal, S., Pratt, A., & Jabbar, M. (2008)24 found that milk was being sold

through three major milk marketing channels including direct sales to individual consumers,

informal private traders and sale through cooperatives and private dairy processors.

Table 3.10: Percentage distribution of quantity of milk sold by type of outlet

Marketing outlet Treatment Control Baseline

Wet

Season

Dry

Season

Wet

Season

Dry

Season

Wet

Season

Dry

Season

Dairy co-operatives 91% 91% 84% 84% 66% 64%

Vendors/traders 5% 5% 6% 7% 15% 16%

Direct Consumers (e.g

neighbors, educational

institutions, hospitals hotels

etc)

2% 2% 7% 7% 21% 20%

Private processors 2% 2% 2% 2% 4% 4%

Others (e.g gifts, donations) 0% 0% 0% 0% 1% 1%


Despite the dairy cooperatives/hubs offering the lowest prices25 compared to the other

channels, it still remained the main channel for most of the respondents. This is because, apart

23 This was largely expected as KMT interventions were largely focused on dairy hubs 24 Staal, S., Pratt, A., & Jabbar, M. (2008). Dairy Development for the Resources Poor - Part 2: Kenya And Ethiopia Dairy Development Case Studies. Rome, Italy: Pro-Poor Livestock Policy

Initiative. 25 Tables 3.25 and 3.26 on revenues


21

from consistently buying all the milk that the farmers supplied, these cooperatives offered other

services to the farmers that made the channel more attractive than the others. Such services

included access to quality feeds and AI on credit and payment recovered by check off system. In

some cases, the cooperatives also offered extension services. This increased farmers loyalty and

trust in the co-operatives; hence delivering their milk even if cooperatives offered relatively

lower prices.

Given the importance of the dairy hubs/co-operatives in enabling farmers to sell most of the

milk that they produced coupled with the services offered, this channel remains the most

important in the milk value chain. In that view, it is important to continue empowering them to

be viable business entities which would be instrumental in propelling smallholder farmers from

poverty.

Discussions with dairy hub managers indicated that after aggregating milk from farmers, most of

the DHs sold the milk to different buyers mainly through the formal channels to major milk

processors including New KCC and Brookside. It was however, noted that some dairy hubs

(mainly those that pasteurized their milk at the dairy hubs), also sold through the informal

channels such as milk traders and satellite milk bars or dispensers. Ostensibly processors were

unpopular due to their low price and other unfavorable conditions. For example it was noted

that the informal channels usually offered higher milk prices compared to large milk processors

particularly during the dry seasons. In addition, milk processors required that the dairy hubs

guaranteed certain quantities of milk supplies on daily basis at a given price while any surplus

could only be offtaken at a lower price. In some instances (particularly during the wet season)

the milk processors were unable to offtake all the milk collected by the dairy hubs even where

a contract existed. Such cases led to DHs maintaining informal traders as their offtakers in

addition to the processors. Given this reality, it was noted that KMAP’s focus on developing the

informal milk channel through interventions that targeted informal milk traders and retailers

was well aligned and justified.

3.7.1.3 Volumes of Milk sold

The daily milk sales through hubs for the treatment group were higher compared to the

control group. This is illustrated in Table 3.11below.

For the treatment group, the average volume of milk sales per day per farmer/household was

21.3 litres during the wet season and 16.7 litres during the dry season. This translated to an

average of 19.1 litres for all seasons which was a 14% increase from the baseline value of 16.3

litres. Among the control group households, the daily quantities of milk sold to hubs averaged

15.1 liters and 13.4 litres during the wet and dry seasons respectively.

The increase in percent of milk sold may be mainly due to increased loyalty of the members in the treatment hubs as a result of improved services delivery, as well as KMAPs intervention of

enhancing linkages between small holder dairy farmers with marketing channels.


22

Table 3.11: Volume of Milk Sold per Day

Source: Dairy farmers’ household survey Name of

dairy hub

N Av Amt

Sold (Wet Season Oct_Dec)

N Av Amt

Sold (Wet April_June)

Average

Wet season

N Av Amt

Sold in Dry Season(Jan-March)

N Av Amt

Sold Cold season (July_Sept)

Average

Cold & Dry seasons

Average all

seasons

Treatment

1 Limuru 32 27.3 32 26.0 26.7 32 21.4 30 21.3 21.4 24.0

2 Gatamaiyu 39 15.7 39 14.6 15.1 37 13.5 36 13.6 13.5 14.3

3 Mkulima bora 39 21.7 40 21.6 21.7 40 19.5 40 19.5 19.5 20.6

4 Tumaini 38 15.7 38 15.7 15.7 38 13.7 38 14.8 14.3 15.0

5 Rugendo 39 23.3 39 23.9 23.6 39 20.1 39 20.3 20.2 21.9

6 Torongo 39 22.4 39 25.3 23.8 39 16.3 39 17.8 17.0 20.4

7 Ololunga 39 25.9 39 28.5 27.2 36 18.3 36 21.7 20.0 23.6

8 Nyala 36 22.9 36 22.8 22.9 36 17.4 35 17.8 17.6 20.2

9 Njabini 37 13.4 38 14.3 13.8 36 10.3 35 10.8 10.5 12.2

10 Karati 36 19.7 35 19.3 19.5 38 12.9 37 12.0 12.5 16.0

11 Siongiroi 42 21.4 42 22.5 21.9 42 14.4 41 15.3 14.8 18.4

12 Meru Multipurpose

33 23.1 34 23.5 23.3 33 22.6 34 21.5 22.1 22.7

Overall

averages

449 21.0 380 21.5 21.3 377 16.7 374 17.2 16.9 19.1

Control

1 Kabete 41 22.4 41 22.5 22.5 41 21.1 41 24.0 22.6 22.5

2 Olbutyo 38 8.9 40 10.6 9.7 22 7.8 21 7.1 7.5 8.6

3 Umoja 38 10.7 38 11.9 11.3 34 10.6 39 10.6 10.6 10.9

4 Nandarasi 35 13.5 37 11.6 12.6 36 11.5 36 10.9 11.2 11.9

5 Kahuru 39 18.9 39 19.6 19.3 37 14.6 37 15.5 15.0 17.1

Overall averages

191 14.9 195 15.2 15.1 170 13.1 174 13.6 13.4 14.2


23

3.7.2 Dairy Production Costs

The overall cost of production for a household within the treatment group was Kshs. 9,180 per

per cow per month or Kshs. 137.7 per cow per day. Given an average yield of 8.9 litres per

cow per day, this would translate to Kshs. 15.5 per litre. For the control group, the monthly

cost per cow was Kshs. 8,836 or Kshs 141.6 per cow per day. Given a daily yield of 8.4 litres

per cow per day, this would translate to Kshs. 16.9 per litre.

Costs of labour, hay and fodder as well as feed supplements comprised the largest share of

monthly production costs among the treatment group households while feed (hay and forage)

comprised the highest cost component for the control group households (Table 3.12).


24

Table 3.12: Average costs of production (variable costs) Name of

dairy hub

Av

Cost hired labour

Av Cost

Feed - hay and fodder

Av Cost

Feed - supplements

Av

Cost Water

Av cost

treatment &pest control

Av Cost

Transport (inputs &milk)

Av

Cost Electricity

Total

Costs

Averag

e No.of cows

Average

cost per cow/month

Average

cost per cow/day

Averag

e cost per litre

Limuru 10,099 7,739 12,370 1,122 1,036 2,613 1,346 36,326 4 9,463 82.2 11.9

Gatamaiyu 6,150 3,689 5,635 197 625 330 193 16,818 2 8,105 130.2 16.1

Mkulima

bora

8,513 12,933 8,958 348 396 1,737 322 33,208 2 18,976 361.4 26.8

Tumaini 7,153 3,360 3,173 203 403 442 208 14,943 2 8,966 179.3 17.1

Rugendo 7,833 8,667 5,122 233 398 512 265 23,030 2 11,976 207.6 15.4

Torongo 8,460 11,039 5,912 1,183 1,568 1,823 - 29,985 5 6,096 41.3 7.6

Ololunga 4,474 5,601 2,546 4,000 1,310 2,441 1,400 21,772 6 3,848 22.7 4.3

Nyala 6,102 2,768 3,544 333 1,266 556 552 15,120 3 5,863 75.8 8.1

Njabini 7,436 6,812 7,995 1,106 283 2,136 1,150 26,918 2 12,146 182.7 23.4

Karati 7,705 2,694 7,137 - 4,250 437 - 22,224 2 10,827 175.8 20.0

Siongiroi 3,648 2,247 2,892 750 664 1,261 1,533 12,996 4 3,483 31.1 5.3

Meru Multipurpose

7,366 6,835 4,455 1,150 338 1,483 564 22,190 2 10,410 162.8 13.1

Average 7,078 6,199 5,812 885 1,045 1,314 628 22,961 3 9,180 137.7 14.1

Kabete 5,927 6,042 6,838 311 717 1,200 389 21,424 2 8,612 115.4 9.0

Olbutyo 3,473 3,760 2,575 311 304 771 390 11,585 2 4,866 68.1 11.4

Umoja 4,746 7,807 3,498 356 300 505 387 17,599 2 9,599 174.5 19.6

Nandarasi 4,898 14,867 4,204 533 792 783 581 26,658 2 14,505 263.1 33.7

Kahuru 6,215 4,878 4,213 400 628 422 - 16,755 3 6,600 86.7 12.9

Average 5,052 7,471 4,266 382 548 736 349 18,804 2 8,836 141.56 17.33



25

3.7.3 Revenues and Gross Margins from Milk production

Gross margins were determined as the net of milk revenues less the total variable costs (direct

costs of milk production and sales (ie GM = TR- TVC)

Where,

TR = Volume of milk * Price of milk

TVC = Summation of all variable costs

Milk sales revenues were higher for the treatment group farmers than for the control group

farmers. One of the expected impacts of the KMAP program is to increase the incomes of the

dairy farmers. The average revenue from milk sales per cow per day averaged Ksh. 334.2 during

the wet season and Ksh 286.1 during the dry season (Table 3.13). On the other hand, the

average revenues for the control group averaged Kshs. 265.4 during the wet season and Kshs.

240.2 during the dry season (Table 3.14).

As a result of higher milk yields and better prices, the gross margins per cow per day for both

the treatment and control groups increased with the treatment group receiving higher gross

margins of Kshs. 196.5 during the wet season and Kshs.148.4 during the dry season (Table

3.13).. In comparison, gross margins for control group members averaged Kshs. 123.9 during the wet season and Kshs. 98.7 during the dry season (Table 3.14).

Table 3.13: Average Milk Revenue for Treatment Group

Outlet Treatment

Wet Season Dry Season

Average Quantity Sold (L)

Average price (Ksh)

Revenue (Ksh)

Average Quantity Sold (L)

Average price (Ksh)

Revenue (Ksh)

Dairy co-operatives 9.5 31.5 300.2 7.4 31.5 234.6

Private processors 11.2 34.0 381.2 9.2 36.0 330.5

Vendors/traders 4.5 37.5 168.2 4.2 38.5 162.6

Consumers 3.9 39.5 153.0 3.1 41.5 130.7 29.1 35.6 1,002.6 24.0 36.9 858.4

Average No. of dairy cows

3.0

3.0

Average revenue per cow per day

334.2

286.1

Average variable costs per cow per day

137.7

137.7

Gross Margin per cow

per day

196.5

148.4



26

Table 3.14: Average Milk Revenue for Control Group

Outlet Control

Wet Season Dry Season

Average

Quantity Sold (L)

Average

price (Ksh)

Revenue

(Ksh)

Average

Quantity Sold (L)

Average

price (Ksh)

Revenue

(Ksh)

Dairy co-operatives 5.4 30.6 166.4 4.6 31.8 146.6

Private processors 4.1 29.8 122.0 3.9 30.0 115.9

Vendors/ traders 3.7 33.8 124.2 3.6 33.9 122.7

Consumers 3.6 32.9 118.2 2.9 32.9 95.2

Total 16.8 31.8 530.8 15.0

480.4

Average No. of cows

2.0

2.0

Average revenue per cow per day

265.4

240.2

Average variable costs per cow per day

141.6

141.6

Gross Margin per head per

day

123.9

98.7


3.7.3.1 Farmers Access to Information and Inputs

Market information is a key factor in smallholder dairy farming enterprises. Smallholder farmers

suffer from insufficient information on the available yield enhancing technologies as well as

lucrative markets. This midline evaluation sought to understand the level of information

availability to dairy farmers on production and marketing. KMAP program endeavoured to build

capacity of DHs and also encourage them to create avenues for sharing different kinds of useful

information with their members.

Figure 3.2: Market Information and Participation in Marketing Activities


Data from the household survey illustrates that 76% of the treatment group and 65% control

group of farmers had heard or seen marketing activities, promotions or advertising related to


27

dairy farming production or marketing that were used during the intervention period (Figure

3.2). This is an indication that 76% (28,12026 farmers) of the beneficiary farmers that benefitted

from supply chain interventions had access to information and inputs.

This is an indication that there are endeavours to disseminate information publicly regarding

dairy production and marketing. In terms of participation in these marketing activities, 30% of

the treatment farmers and 47% of the control group farmers had participated in these kinds of

activities. The results revealed that the farmers were able to gather a wide range of information

including quality feeds, hay, concentrates, silage, milk quality, veterinary services and herd

management.

Table 3.15 presents the different sources of information related to the three main yield

enhancing inputs: hay, concentrates and AI. The results indicate that the DHs were the main

sources of information to the dairy farmers. Our results shows that 44% and 39% of the

treatment and control group; 42% treatment farmers and 36% control group and 40% of the

treatment farmers and 33% of the control received information on AI, Concentrates and Hay

respectively from the DHs. This was corroborated by the information from key informant who

acknowledged that DHs organised for education days at least once per year for their farmers.

Other DHs had a structured extension department that offered extension services to their

members systematically.

One of the interventions of the KMAP program is improving the informal supply chain services

by working with agents and traders. From the study, only 6% (1,92727 farmers) accessed

information and inputs from traders and agents.

Table 3.15: Sources of Information Source of Information AI (%) Concentrates (%) Hay (%)

Treatment Control Treatment Control Treatment Control

Cooperative/ dairy hub 44 39 42 36 40 33

Milk trade association 3 1 4 2 3 1

Processors 1 1 2 4 4 5

Agro vets/Animal feeds outlets 18 17 20 24 16 14

Village agents/traders 6 3 6 5 7 6

Other farmers 15 20 14 17 19 24

Community events 2 4 3 4 4 6

Demonstration plots/farms 3 1 5 2 6 2

Private vets 10 14 4 6 2 10


26 76% of total number of beneficiaries (37,000) 27 6% of beneficiaries of feed component (32,067)


28

3.7.3.2 Frequency and Consistency of Information Access

This evaluation also sought to understand the progress made on the consistency of receiving

this information. This is because for these pieces of information to be useful, they ought to be

disseminated consciously and consistently.

Figure 3.3: Consistency and Frequency of information


The evaluation results reveal that information regarding AI, feed concentrates and supplement

was received by majority of the farmers most recently. At least 37% of the treatment group

farmers and 25% percent of the control group farmers had received information on

concentrates within the last week. Similarly, 30% of farmers participating in KMAP and 21% of

the control group farmers received information on AI in the same period of time. However, the

information on hay had been received slightly over a year ago as noted by 31% of the treatment

group farmers, and 10% of the control group. Figure 3.4 gives more details on how the

information on the different feed products had been accessed.

3.7.3.3 Level of Satisfaction with Sources of Information

Availability of information alone does not suffice, the information accessed should be easy to

comprehend and be useful to the consumers. Figure 3.4 gives details on the level of satisfaction

the farmers expressed on the information they received. This was measured using a likert scale


29

of between 1 and 5, where 1 is very dissatisfied and 5 is very satisfied. The results indicate

majority of the farmers were satisfied with the information they received. However, there was

a slightly higher proportion the treatment farmers reporting high level of satisfaction compared

to the control group farmers. For treatment farmers, the satisfaction index ranged from 4.1

(82%) for information on AI services, and 4.2 (84%)each for concentrates and supplements and

hay, while for the control group farmers the satisfaction indices were 3.7, 3.9, 4.0 for

information on hay, AI services and concentrates and supplements respectively.

4.2 4.2

4.1

3.7

4.0

3.9

3.4

3.5

3.6

3.7

3.8

3.9

4

4.1

4.2

4.3

Hay Concentrates and supplements

A.I Services

Satisfaction Index with Source of Information

Treatment

Control

Figure 3.4: Level of satisfaction on information received


3.7.3.4 Level of Satisfaction with Supply Chain Services

The level of satisfaction with supply chain services was measured using four main attributes:

price, timeliness of milk collection, timeliness of milk payment and information received from

processors/dairy hubs. This was measured using a scale of between 1 and 5, where 1 is very

dissatisfied and 5 is very satisfied. The overall satisfaction index with supply chain services for

the treatment group was 3.5 (70%), higher than that of the control group at 3.1 (62%) as shown

in Table 3.16. This implies that 70% of the beneficiaries are accessing improved supply chain

services, which are 25,900 farmers28 .

Farmers in the treatment group had benefitted from KMAP interventions of improving the

operations of the dairy hubs, thus making the hubs more responsive to the farmer needs, hence

higher proportions accessing improved supply chain services than control group farmers. High

satisfaction levels were exhibited more in the information farmers received from dairy hubs

(3.9) as well as with the timeliness of milk collection (3.8) and milk payment (3.6). The lowest

levels of satisfaction were experienced with the pricing of milk. This is an indication that

28 70% of total number of beneficiaries (91,865)


30

although farmers sold their milk to the cooperatives, the attraction to deliver milk to

cooperatives was not necessarily due to price, but due to other benefits as noted earlier in this

report. However, it is important for supply chain players to discuss ways of making the price of

milk better for the farmers.

Table 3.16: Level of Satisfaction with Supply Chain Services

Attributes Level of satisfaction

Treatment (N=473) Control (N=206)

Price offered for milk 2.6 (52%) 1.9 (38%)

Timeliness of milk collection 3.8 (76%) 3.7 (74%)

Timeliness of milk payment 3.6 (72%) 3.3 (66%)

Information I get from the milk processors/dairy hubs 3.9 (78%) 3.5 (70%)

Average 3.5 (70%) 3.1(62%)


3.7.3.5 Level of Satisfaction with Inputs Provision

The evaluation also sought to establish the satisfaction of the farmers with inputs provision: hay,

feeds and concentrates and AI services on a number of attributes. These attributes include:

price, quality, availability, and trust of the vendors. The level of satisfaction was measured using

the same methodology used for measuring the level of satisfaction with supply chain services.

The level of satisfaction with inputs provision to farmers was between 3 and 4 for both the

treatment and control groups, with the highest levels of satisfaction being observed for AI

services and the lowest for hay provision (Table 3.17). The biggest challenge the farmers had

was with the high price of inputs. It is therefore important for the service providers to offer

competitive prices for the inputs. It is noted that the farmers both in the treatment and control

groups exhibited a high trust in the vendors of inputs. This may be attributed to the fact that

most of the farmers purchased their inputs from either the dairy hubs or vendors

recommended by the dairy hubs, hence assured of quality inputs.

Table 3.17: Level of Satisfaction with Inputs Provision

Attributes

Level of Satisfaction

Hay Feeds and Concentrates A.I. Services

Treatment Control Treatment Control Treatment Control

Price 2.5 2.5 2.6 2.6 3.0 2.6

Quality 3.7 3.6 3.9 3.9 3.8 3.8

Availability 3.7 3.4 4.0 4.0 3.9 3.8

I trust the vendors 3.8 3.5 4.0 4.0 4.1 4.0

Average 3.4 3.3 3.6 3.6 3.7 3.6


3.7.4 Market Systems Change Level for the Supply Chain Component

Qualitative analysis of the sampled treatment and control dairy hubs revealed that the dairy

hubs offered a myriad of services to their farmers including:

• Veterinary and AI services;

• Concentrates and supplements on credit;


31

• Extension services/training and advisory services;

• Seeds and fertilizers;

• Loans and linkages to credit;

• Mobile money – advances through mobile phones;

• Transport services;

• Quality checks for milk;

• Provision of marketing information;

• Provision of hay29 or linking farmers with commercial hay providers; and

• Farm machinery and equipment.

Following the KMAP interventions on supply chain, the performance in most of the intervention

dairy hubs improved (Table 3.18)30. Overall nine (9) of the intervention dairy hubs had

membership numbers growing by between 8% and 317%. Quantity of milk handled also

increased in seven (7) of the dairy hubs by between 11% and 94%. Turnover increased by

between 2% to 100% in eight (8) of the dairy hubs.

Table 3.18: Performance of the Intervention Dairy Hubs Growth

(2013-2015)

Nyala

Nja

bin

i

Kara

ti

To

ron

go

Sio

ngir

oi

Tu

main

i

Lim

uru

Mku

lim

a

Bo

ra

Ru

gen

do

Olo

lolu

nga

Gata

maiy

u

Membership 27% 36% 44% 8% 26% No data 15% 317% No data 8% No data

Quantity (Litres)

11% 82% 75% 0% 11% No data -1% 94% 50% 46% No data

Turnover (KES)

7% 83% 11% -16% 16% No data 14% 2% 100% 31% No data

Source: Dairy Hubs Managers

Other benefits reported by the dairy hubs included:

• Increased customers and sales;

• Improved stocks display and management;

• Improved access to financial resources;

• The dairy is now more focused on the growth path due to having strategic plan;

• Market research plan has enabled them to venture into new markets;

• Training on customer service helped reduce complaints from farmers and other clients

The market systems change for the supply chain intervention is discussed further based on the

Adopt Adapt Expand and Respond (AAER) model as illustrated in Annex 10.

29 This applies only to Nyala Farmers Dairy Co-operative 30 More details in appendix 4.


32

3.8 Results for Improving and Promoting AI Service Component

Animal genetics is a critical input in dairy production for the purposes of improving production

given different environments. Better/dual breeds that feed less and still produce more –milk and

meat and able to adapt to various ecological zone/climate change and tolerant to many livestock

disease etc are crucial. This is another key intervention area of the KMAP project with the aim

of stimulating greater demand for AI and veterinary services and ensuring access to and

adoption of quality AI and animal health services by small holder dairy farmers.

KMAP enhanced partnerships between dairy hubs and processors and the private sector

providers, County Governments and LGSA to improve AI service providers customer service

skills in 11 dairy hubs so as to build customer confidence in AI services. As a result AI service

providers affiliated to 8 dairy hubs and 2 processors invested in promotional and marketing

activities with relevant market actors. These efforts bore fruit because accredited AI service

providers reported increased AI services provision by 61% between 2013 and 2016, implying an

increase in number of farmers adopting A.I services.

3.8.1 Target Group Change Level for Improving and Promoting AI Service

Component

3.8.1.1 Awareness of Accredited AI Services

From the midline evaluation, there was a high awareness of accredited A.I services, with

awareness levels of 75.9% and 67% for the treatment group and for the control group

respectively. The source of awareness was mainly through the co-operative/dairy hub as

indicated by 45.4% of the farmers in the treatment group and 43.2% of the farmers in the

control group (Figure 3.6). Other major sources of awareness include agrovets, other farmers

and village agents.

Figure 3.6: Source of awareness of Accredited AI Services



33

3.8.1.2 Usage of Accredited AI Services

There was considerably high usage of the accredited AI services, 72.7% of the treatment

farmers and 57.8% of the control group farmers used accredited AI services. It is important to

note that the level of usage for the accredited AI services is higher for the treatment group

than the control group (Figure 3.7). Similarly, the usage of accredited AI services has more than

doubled from 35.3% in the baseline to 72.7% at the midline. This may be attributed to KMAP’s

strategy of leveraging the interconnectedness of various market systems that are important to

dairy, specifically breed improvement (through provision of accredited AI services), feed, and

vet services. The midline evaluation found out that most of the DHs provided accredited AI

services to their members on check-off system in one way or another. Two arrangements were

common; first where the dairy hub accredits a number of AI service providers (SPs) who the

members use on credit. The second one is where the dairy hubs have a vet and AI services

departments in their structure, they employ experts who serve their members on credit. These

arrangements are instrumental in availing quality AI services to members and thus scaling up the

use.

Figure 3.7: Usage of Accredited AI Services


3.8.1.3 Cost of Accredited AI Services

Accredited AI service providers charge the following per insemination: Local semen- Ksh. 1000;

Exotic-Ksh. 1000-3000; Sexed- Ksh 6000-700031. The average price of A.I services is Ksh 1,578

(Maximum Ksh 8,000). These costs are quite high for most of the farmers. There is therefore

need to subsidize these costs so as to further increase the adoption of accredited A.I services.

It is also important to open up more space to more actors to provide accredited AI services.

When compared with the baseline, the average prices was Ksh 1,277 (Maximum Ksh 3,600)

when provided by agrovets and Ksh 1,768 (Maximum Ksh 3,000) when provided by private

vets.

31 Source: Key informant interviews with Independent and Dairy Hub based AI providers


34

3.8.1.4 Level of Satisfaction with Accredited AI Services

The evaluation also sought to understand the level of satisfaction of farmers on a number of

indicators related to AI service delivery and utilisation (affordability, quality, availability, and

trustworthiness). Tables 3.19 and 3.20 reflect the levels of satisfaction relating different aspects

on AI services for the treatment and control groups respectively. On average, there was an

increase from 16.8% at the baseline to 35.8% of the farmers in the treatment group that “strong

agreed” that they were satisfied with the affordability, quality, availability, and trustworthiness of

the A1 services (Table 3.19). The control group also had a lower satisfaction level at 20.1% as

compared to the treatment group (Table 3.20). However, it is noteworthy that both the

treatment and control groups had a problem with the pricing of the AI services because it is the

aspect that exhibited the lowest satisfaction levels. AI services should therefore be made more

affordable.

Table 3.19: Agreement Scale on Satisfaction with AI Services (Treatment)

Attributes

Level of Satisfaction (Treatment)-Midline (%)

Baseline

(Strongly

agree) Strongly

disagree Disagree

Neither

agree

or

disagree

Agree Strongly

Agree

I am satisfied with the price

of artificial insemination

services

19.4 23.2 13.6 19.2 24.6 15

I am satisfied with the quality

of artificial insemination

services

9.5 5.5 10.5 36.7 37.8 15

I am satisfied with the

availability of artificial

insemination services

6.7 6.7 7.7 40.6 38.3 19

I trust the providers who sell

me artificial insemination

services

3.3 6.3 14.5 33.3 42.6 19

Average 9.7 10.4 11.6 32.5 35.8 17

Table 3.20: Agreement Scale on Satisfaction with AI Services (Control)

Attributes Level of Satisfaction (Control) - (%) Baseline

(Strongly

agree) Strongly

disagree

Disag

ree

Neither

agree or

disagree

Agre

e

Strongly

Agree

I am satisfied with the price of

artificial insemination services

24.3 31.6 4.6 34.2 5.3

I am satisfied with the quality of


6.6 12.5 13.9 47.4 19.7

I am satisfied with the availability

of artificial insemination services

3.9 11.2 8.6 51.3 25.0

I trust the providers who sell me


1.3 13.2 9.2 46.1 30.3

Average 9.0 17.1 9.1 44.7 20.1 15.5



35

3.8.1.5 Perceived Benefits of using Accredited AI Services

Both treatment and control group farmers appreciated the benefits of the use of AI to their

dairy enterprises. The main benefits mentioned by farmers in the treatment group include:

increased milk production (77.5%); healthier calves (75.7%); high conception rates (68.6%);

increased incomes (63.2%). For the control group increased milk production (57.1%) and

healthier calves (50.4%) were the main benefits accruing to use of AI services (Table 3.21).

Table 3.21: Benefits of using Accredited AI Services

Benefits Treatment Control

N % N %

Increased milk production from

offspring 259 77.5 68 57.1

Increased incomes from milk sales

(offspring’s) 211 63.2 45 37.8

High success rate (Conception) 229 68.6 59 49.6

Higher birth rate 167 50.0 34 28.6

Easier birth process 182 54.5 27 22.7

Healthier calves 253 75.7 60 50.4

Less variation in milk production 117 35.0 4 3.4

None 1 .3 1 .8

Note: The percentages are for a multi response variable and therefore they do not add up to 100%


3.8.2 Market System Change Level for Improving and Promoting AI Service

Component

3.8.2.1 Success Rate of AI Services

The success rate of the AI services was calculated by getting the proportion of cows with live

births for the cows artificially inseminated. This is illustrated in Table 3.22 below. The success

rate for AI services for the treatment farmers marginally increased by 3.8%, from 75.4% at the

baseline to 79.2% at the midline. It is also observable that the success rate is higher for the

treatment group than the control group. This may be attributed to the fact that there are more

farmers in the dairy hubs under MAP intervention using accredited AI services than both in the

control group and at the baseline (Figure 3.8).

Table 3.22: Success Rate of AI Services

Statistics Treatment Control

Mean N Total Mean N Total

Number of cows artificially inseminated 2 318 636 2 125 250

Number of cows with live births 2 252 504 2 85 170

Success Rate (Midline)

79.2%

68.0%

Success Rate (Baseline)

75.4%

% change 3.8%



36

3.8.2.2 AI Service Providers with Improved Customer Service and Business Skills and

Investing in Promotional and Marketing Activities with Relevant Market Actors

Accredited AI providers also reported that the KMAP program had increased the number of

clients due to referrals as well as customer loyalty. This is illustrated by the increase in the

number of AI servings (Table 3.23). Overall, the number of AI servings grew by 61% between

2013 and 2016.32 The increase in growth was observed for all the AI providers sampled in the

evaluation except for KCC Runyenjes experiencing growth in the number of AI services

provided, thus implying that they served an increased number of customers.

Table 3.23: Trends in Provision of AI Services33 (No. of customers)

AI Provider 2012 2013 2014 2015 2016 % Growth

(2013-2016)

Limuru 600 780 900 1,440 1,800 131%

Farmlink-Embu 600 600 650 720 700 17%

Meru Central 4,800 5,400 6,000 6,600 7,200 33%

Caritas Embu 6,600 2,700 2,700 5,400

100%

KCC Runyenjes 1,200 1,200 600 540

-55%

Nyala 3,120 3,600 4,200 4,200 4,200 17%

Karati 480 720 840 1,080 1,200 67%

Nandarasi 3,161 3,661 4,112 5,000 5,050 38%

Torongo 50 50 60 80 120 140%

Siongoroi 240 456 636 720 1,020 124%

Overall 61%

Source: Dairy hubs managers’ interviews

The market system change for improving and promoting AI services are further on the Adopt

Adapt Expand and Respond (AAER) model is illustrated in Annex 10.

3.9 Results for Commercial Hay Production

3.9.1 Background of Commercial Hay Production Intervention

The KMAP project intended to develop dairy value chain by assisting farmers’ access and use

quality inputs for their dairy farming, a crucial aspect in promoting productivity and helping

farmers cope during times of drought. One of the most important inputs of dairy farming in the

KMAP program is hay. KMAP’s intervention included: support commercial hay producers to

improve production, expand their business and adopt faster growing fodders (hay); support

feed manufacturers to develop formulation strategies based on quality fodder, concentrates and

supplements and build the capacity of labs to market and co-brand quality feed.

32 Note: AI success rates are 79.2% 33 Estimated by number of AI straws used


37

3.9.2 Target Group Change Level for Commercial Hay Production Component

3.9.2.1 Awareness of Quality Assured Hay

Hay is a very useful fodder for dairy farmers because it can be dried and preserved for use in

the dry season. It is also has very high levels of nutrition and thus very ideal. Thus, the KMAP

project has been working with commercial hay producers to increase its supply and availability.

The results show that on-farm production of hay was still low, at only 28% for the treatment

group and 27% for the control group (Table 3.20). In view, farmers supplemented this through

purchase in which 50% treatment and 27% control group farmers said that they purchased hay.

KMAPs’ efforts are not only focused on the supply of hay but also the quality of the hay

produced. From the midline survey 41% of the treatment and 63% of the control group were

aware of quality assured hay.

Table 3.24: Hay Production

Treatment Control

N Midline% Baseline

%

N Midline

%

Baseline

%

Do you grow grass for Hay (Yes) 132 28 56 27

Do you purchase hay (Yes) 236 50 56 27

Aware of quality assured hay (Yes) 192 41 9.3 31 63 10.7


3.9.2.2 Utilization of Quality Assured Hay

KMAP intended to increase the availability, access, and use of hay, the reason was to bridge

scarcity of fodder during dry season to prevent reduction in milk production. A key

intervention of the program was to not only ensure access to hay, but also emphasis on the

quality. The evaluation results indicate a huge proportion of the farmers that purchase hay, go

for quality assured hay, 93.2% and 61.5% for the treatment group during the dry and wet

season respectively (Table 3.25)34. On average 77% (24,083) 35 of the beneficiary farmers access

quality assured hay during both the wet and dry seasons. For the control group, the

proportions are slightly lower than for the treatment group; however, the difference is not much.

Findings from interviews with dairy hub managers revealed that all the dairy hubs in Kiambu and

Embu counties did not stock hay for their farmers and thus only sourced it from the traders

and agro-vets. In the central region it is only in Nyala dairy hub in Nyandarua where the KMAP

initiative on production and selling of hay was being practiced. However, in the Rift Valley,

Torongo and Siongiroi had arrangements of stocking hay and availing it to their members.

However, it was noted that KMT had made efforts to work with the commercial hay producers

34 The differentials between the use of quality assured hay during the dry season and wet season is

because more famers buy hay during the dry season than the wet season 35 Proportion of farmers selling*Total beneficiaries feeds component (32,067)


38

such as the Technology Farm in the Rift valley both to train farmers on hay production, and

also to ensure that there was enough quality hay for the market.

Table 3.25: Proportion of Farmers that Purchase Quality Assured Hay

Season Treatment (%) Control (%)

N Purchase Quality

Assured Hay

(Yes)

N Purchase Quality

Assured Hay

(Yes)

Wet season (Oct-Dec) 120 60.0 41 56.1

Wet Season (April-June) 108 63.0 44 56.8

Average for wet season

61.5

56.5

Dry Season (Jan-Mar) 120 96.7 46 87.0

Cold and dry season (July-Sept) 106 89.6 35 88.6

Average for dry season

93.2

87.8


3.9.2.3 Perception on the Quality of the Hay Purchased

One of the main indicators of the KMAP program for the commercial hay production

component is to establish the proportion of farmers claiming to purchase ‘very good’ quality

hay during the wet and dry season. This is illustrated in Table 3.26 below. The proportion of

the farmers in the treatment hubs claiming to be using “very good quality hay” during the wet

season was 45.6%, higher than the baseline value (35.4%) and the control group (26.3%). The

trend is similar for the dry season with the proportion of farmers claiming the hay they used is

‘very good quality’ being 40.3% as compared to 23.5% and 29.5% for the midline control group

and for the baseline respectively. These results show that there is a great improvement in

accessing quality hay for the farmers under the dairy hubs benefiting from the KMAP

interventions.

Table 3.26: Quality of Hay Purchased by Season Season Treatment group (Midline) Baselin

e (Very

good quality

(%)

Control group

(Very good quality)

(%)

Very poor

quality (%)

Poor quality

(%)

Okay quality

(%)

Mostly good

quality (%)

Very good

quality (%)

Wet season (Oct-Dec) 0.0 8.5 25.8 20.5 45.2

31.8

Wet Season (April-June) 3.4 6.9 21.6 22.1 46.0

20.8

Average for wet season

45.6 35.4 26.3

Dry Season (Jan-Mar) 0.9 4.7 27.7 24.5 42.1

26.3

Cold and dry season (July-Sept) 12.2 2.2 27.8 19.4 38.4

20.7

Average for dry season

40.3 29.5 23.5

Average all seasons 42.5 32.5 24.7


3.9.2.4 Types of Hay Purchased

The main types of hay purchased by the farmers were the grass and leguminous hay. The grass

hay included Rhodes, ryegrass, brome and orchard while the leguminous hay was mainly


39

Lucerne, berseem, cowpea and soybean. Of the sampled treatment farmers who used hay, 58%

utilized grass hay, while 39% used the leguminous hay (Figure 3.8).

Figure 3.8: Types of Hay Purchased


3.9.2.5 Sources of Hay

The midline results show that majority of the treatment farmers sourced their hay from their

DHs as indicated by 54% and 56% of the treatment group farmers in the wet and dry seasons

respectively (Table 3.27). On the other hand, most of the control group farmers and farmers

during the baseline procured hay from mostly from traders during the wet and dry seasons.

This implies that the KMAP interventions through working with dairy hubs is an effective

mechanism for availing quality assured hay to farmers. It is therefore important to enhance the

capacity of dairy hubs to ensure that farmers have access to quality assured hay to its members.

It was further noted that most of the dairy hubs in Kiambu, Embu and Nyandarua County

except did not stock hay to supply to their farmers. Only Nyala cooperative supplied their

members with hay. In the Rift Valley region, Torongo and Siongiroi DHs stocked hay and

supplied it to their farmers. Siongiroi DH had contracted farmers to supply hay to their stores.

This should be encouraged for the other Dairy hubs.

Table 3.27: Sources of Quality Assured Hay Source of Hay Wet season Dry season Baseline

Treatmen

t Control Treatment Control

Wet

season

Dry

season

Dairy hub/co-operative 54.0% 20.5% 56.0% 22.4% 18.2% 14.7%

Agrovet 32.8% 18.1% 29.6% 23.7% 24.2% 24.7%

Traders/agents/Trader association 45.2% 49.7% 34.9.3% 48.0% 55.6% 54.2%

Other farmers/neighbours 5.9% 21.6% 8.5% 13.4% 9.1% 11%



40

3.9.2.6 Level of Satisfaction with Quality Assured Hay

The midline results revealed that, overall the farmers in the treatment group had a higher

proportion (28.5%) of those who ‘strongly agree’ with the statements measuring satisfaction

(affordability, quality, availability, trustworthiness, willingness to continue purchasing hay) than

the control group farmers (20.9%) and the baseline (15.7%). This is illustrated in Table 3.28. It is

however important to note that the level of satisfaction related to price was low for farmers

both in the treatment and control group. Interventions that can lead to affordable quality

assured hay would go a long way in improving the efficiency for milk production.

Farmers showed willingness to continue using hay but emphasized on availability of good quality

hay. For treatment farmers, 45.8% indicated they strongly agreed that they will buy hay if the

quality is assured. A huge proportion of the farmers strongly agreed that they will continue and

also increase purchase of hay in the future. There was almost a similar trend on the control

side, 50% of the farmers strongly agree that they will buy hay if the quality is assured. A

considerable proportion agreed that they were willing to increase the purchase of hay in the

future.

Table 3.28: Agreement Scale on Satisfaction with Hay

Attributes

Level of Satisfaction (Treatment)-% Midline Control

(Strongly agree)-%

Baseline (Strongl

y agree)-%

Strongly disagree

Disagree Neither agree or disagree

Agree Strongly Agree

I am satisfied with the price of hay

33.5 18.8 15.7 23.0 8.9 1.8 9.7

I am satisfied with the

quality of hay

2.6 16.9 17.5 37.6 25.4 13.8 12.3

I am satisfied with the availability of hay

11.6 9.0 9.0 43.4 27.0 15.8 14.5

I trust the providers who sell me hay

3.2 11.1 18.9 39.5 27.4 9.3 15.2

I will continue to

purchase hay in the future

10.2 7.5 11.2 40.6 30.5 26.4 16.1

I plan to Increase my

purchases of hay in the future

10.0 8.4 10.0 36.8 34.2 29.1 18.1

If quality assured hay is made available in the market, i will purchase it

6.8 5.8 7.4 34.2 45.8 50.0 24.2

Average 11.1 11.1 12.8 36.4 28.5 20.9 15.7



41

3.9.2.7 Perceived Benefits of Quality Assured Hay

Farmers in both the treatment and control groups rated increased milk production, increased

incomes, healthier cows and higher quality milk production as the perceived benefits of using

quality assured hay. This is presented in Table 3.25 below.

Table 3.29: Benefits of using Quality Assured Hay from farmers’ perception

Benefits Treatment Control

N % N %

Increased milk production 148 85.1 64 90.1

Increased income 117 67.2 41 57.7

Less variation in milk production 74 42.5 6 8.5

Higher quality milk produced36 96 55.2 23 32.4

Healthier cows 116 66.7 40 56.3

None 4 2.3 1 1.4


3.9.3 Market Systems Change Level for Commercial Hay Production Component

The market systems changes for commercial hay production based on the Adopt Adapt Expand

and Respond (AAER) model are discussed in Annex 10.

3.10 Results on Program’s Impact on the Farmers/Target Beneficiaries

The expected impact of the KMAP program is job creation and increasing farmers’ incomes.

This section will therefore discuss the increase in farmers’ revenues from sale of milk, as a

measure of incomes as well as review the number of new jobs created. This section will

further discuss the social and economic benefits accruing from dairy farming that have and

impact of building the resilience of the dairy farmers’ households to cope with social, economic

and climate change shocks.

3.10.1 Most Significant Changes to Dairy Farming

The evaluation sought to establish from both treatment and control group farmers the most

significant changes that have occurred to their dairy farming in the last 12 months. This is

illustrated in Figure 3.10 below. Increased milk production, increased incomes, healthier cows,

consistency in milk production and reduced A. I failures/increased conceptions were identified

as the most significant changes in dairy farming experienced by farmers both in the treatment

and control groups.

36 The quality of feeds affects the butter fat content in milk, taste of the milk and the level of aflatoxins in milk. It

should however, be noted that milk quality is also affected by other factors including milk handling (post-harvest), udder management, presence of antibiotics residue etc.


42

Figure 3.10: Most Significant Changes in Dairy Farming


3.10.2 Contribution of Dairy Hubs/Co-operatives to Most Significant Changes to

Dairy Farming

Dairy cooperative/dairy hubs were the most important KMAP’s intervention entry point. It is

therefore imperative to understand just how this contributed to the changes that were

experienced at the farm level. Majority (43% in treatment and 42% in the control) rated

provision of quality trainings / seminars on dairy practises as the main contribution which led to

the significant changes in dairy production (Figure 3.11).

Figure 3.11: Role of Dairy Hubs in Most Significant Changes



43

Farmers attributed the information and training they received from the dairy hubs as an

important factor contributing to increased milk production, reduced AI failures and healthier

cows. Information has been identified as a major factors supporting of inhibiting adoption and

use of improved innovations. So if farmers are able to received quality information from a

trusted source, then they are able to make rational decisions. This is supported by the second

listed factor, that dairy cooperatives/hubs offered good extension services (12% treatment). At

least 18% of the control group also mentioned that the dairy cooperatives provided credit

services/loans. Again, lack of access to affordable credit can inhibit technology adoption,

especially for capital intensive enterprises such as dairy.

3.10.3 Resilience Analysis

The resilience data was collected and analyzed based on 6 livelihood assets/ capitals as defined

by the DFID’s Livelihood sustainability framework (see annexed). The six (6) capitals include:

human capital; increased knowledge and skills; social capital; physical capital; financial capital; and

natural capital. The farmers’ perception of benefits accruing from dairy farming was measured

for each of the capitals using a likert scale of 1 to 5, where 1 represents least benefited and 5

represents most benefited. The overall index for each of the aspect (human capital; increased

knowledge and skills; social capital; physical capital; financial capital; and natural capital was

derived by getting the average indices for each of the benefits describing each capital. This is illustrated in Table 3.30.

Overall, both the treatment and control group had an above average level of benefit on all

aspects measured, with the treatment group experiencing more benefits than the control group

on all aspects (Figure 3.12), albeit with not very big differentials. This may be attributed to the

KMAP interventions that led to farmers in the treatment groups having more incomes from the

sale of milk as well as having more knowledge on dairy production.

Figure 3.12: Role of Dairy Hubs in Most Significant Changes



44

Table 3.30: Social and Economic Benefits

Socio-economic Benefits Treatment Control

Human Capital

Improving health 3.85 3.96

Improving nutrition 3.99 3.98

Improving education 3.97 3.85

Average 3.94 3.93

Increased Knowledge and Skills

Improving capacity to work at the farm/enterprise 3.45 3.05

Improving capacity to adapt and diversify sources of livelihoods 3.46 2.98

Average 3.45 3.01

Social Capital

Increasing networks and connections 3.36 2.90

Improved relations of trust and mutual support 3.53 3.03

Joined formal and informal groups-chamas 3.48 3.15

Improved mechanisms for participation in decision-making and agency 3.21 3.01

Average 3.39 3.02

Physical Capital

Was able to acquire tools and equipment for production , e.g. jembes,

seed, fertilizer, pesticides

3.35 3.38

Was able to acquire means of transport –motor cycle, bicycle,

wheelbarrow, donkey carts, vehicles, etc

3.10 2.69

Secured/improved shelter and buildings 3.18 3.01

Improved Energy-solar, wind, biogas etc 2.90 2.57

Average 3.13 2.91

Financial Capital

Improved Savings, Chamas, Mobile Banking Etc 3.22 3.00

Acquired insurance 2.48 1.96

Expanded/deepened business 2.88 2.53

Able to access credit and reduce debt 3.20 3.06

Reduced reliance on remittances 3.16 3.01

Increased wages 3.10 2.89

Average 3.08 2.87

Natural Capital

Bought or leased more land 2.42 2.13

Developed/acquired improved water resources 2.69 2.72

Planted trees and forest products 2.78 2.52

Average 2.63 2.46



45

4 CHAPTER 4 ACHIEVEMENTS OF PROGRAM INDICATORS

Based on the findings from the impact assessment, a summary of the program achievements

based on the set targets and comparing with the baseline survey values are discussed in this

section.

In terms of the impacts of the program, the average yield per cow per day for the two seasons

stood at 8.9 litres against a target of 10.7 litres, thus missing the set target by 1.8 litres. This is

when compared to the control group whose average was 7.8 litres per cow per day. Since the

average milked cows for the treatment groups were 3, the total daily milk yields were 26.7

litres per household, with an average price of Kshs. 36, this earned the household a total

revenue of Kshs.1, 002 per day on average. After deducting the daily variable costs per cow

which averaged Kshs, 137.7, this further resulted to gross margins of Kshs 196.5 per cow per

day during the wet season and Kshs. 148.4 per cow per day during the dry season.

The number of farmers that accessed information and inputs reached 28,120 by midpoint of the

program against a set target of 44,26437 (63.5% achievement). The trend is similar with the

number of farmers who active members and accessing improved supply chain services reaching

25,900 farmers against a target of 42,203 farmers (61.4% achievement). The number of dairy

hubs with increased access to retail markets was 14 against a set target of 8DHs and 3

processors. However, only 5 dairy hubs and 2 processors improved customer-centric

distribution and retail strategies and new product innovations and marketing strategies out of

the 16 dairy hubs and 5 processors envisaged (31% achievement). New ICT solutions were to

be purchased by 8DHs and 3 processors but only 5 DHs and 3 processors purchased (73%

achievement).Investments in improved supply chain management practices were carried out by

5 dairy hubs against the 20 (25% achievement) envisaged. Energy efficiency technologies were

adopted by 5 DHs and 1 processor as envisaged.

The average price of insemination was also slightly higher at Ksh 1,578 against a target of Ksh

1,316. The higher prices were as a result of increased preference for exotic semen which was

more costly to local semen. The proportion of farmers who used accredited AI services

exceeded by 18% to reach 72.7% against a target of 55% (118% achievement). A total of 218 AI

service providers from 7 Counties invested in customer service training against a set target of

200 (105% achievement). The number of internal and independent providers investing with

relevant market actors in promotional and marketing activities was 12 against a set target of 15

(90% achievement).

The number of farmers accessing quality assured fodder was 24.083 against a target of 14, 850

(171% achievement) while 28.5% of the farmers strongly agreed they were satisfied with the hay

they received against a target of 25%. The proportion of farmers purchasing hay that was

37 This is because the number of farmers benefitting from supply chain services was only 37,000, much less than

the set target.


46

termed as of “very good quality” was reported by 42.5% of the farmers against an envisaged

52.5%. Only two CHPs38 invested in soil testing and with labs in co-branding against a set target

of 5 for soil testing and 2 for co-branding. The two CHPs were also expected to access

working capital/assess financing and/or equity, and this was achieved. More details on the

achievement of targets are illustrated in Table 4.1 below.

38 Hay N Forage and Sochon


47

Table 4.1: Summary of Program Achievements per Indicators Issue/

Level

Key Result Indicators Baseline Mid line 2016

Targets 39

Achievement

Increasing incomes to

farmers from milk

sales

% increase in income from sale of milk

Dry season- 258 per day

Wet season- 278 per day

• Dry season-286.1

• Wet season- 334.2

Not set 17.8% increment during the dry season

80.2% increase during the wet season

Total revenue generated from the sale of milk in Ksh.

Wet season – Kshs.1,002.6 per day per hhd Dry season – Kshs. 858.4 per day per

household40

Not set

Additional revenue generated from the sale of

milk in Ksh.

Wet season – Kshs. 223 per head per day

Dry season –Kshs. 46.1per head per day41

Not set

Dairy- Formal Supply Chain

Target Group Change

Farmers Increase milk sales at farm level42

Total Volume of milk sold in litres

Wet season – 21.3 litres per household Dry season -16.8 litres per household

Not set

Additional volumes of milk sold in litres

Dry season- 14 per day Wet

season- 15.2 per day

Dry season- Additional 2.8 litres per day Wet season- Additional 6.1 litres per

day

% increase in volumes of

milk sold

Dry season-20%

Wet season- 40.1%

No targets

set

Farmers increase milk

yields at farm level43

Aggregate yield per day Dry season- 24

Wet season- 29.1 Average -26.6

No targets

set

Average yield per cow per

day

Dip stick

values(2014)

Dry season- 8.0

Wet season- 9.7

10.7 Target missed by 1.8

litres

39 Sourced from the 2016 projections in the KMT intervention plans 40 Reported per household 41 Per household and per cow 42 Cover all beneficiaries 43 Covers all beneficiaries


48

Issue/ Level

Key Result Indicators Baseline Mid line 2016 Targets 39

Achievement

at 7.02 liters Average- 8.9

Other farmers increase milk yields at

farm level

Average yield per cow per day for non-target farmers

Dry season- 8.7

Wet season- 11.7 Average-

10.2

Dry season- 7.5 Wet season- 9.3

Average-8.4

No targets set

Achievements were lower than the baseline

values

Farmers access better information and inputs

through improved distribution and retail channels

No. of farmers accessing information and inputs

76% (28,12044) 44,264 Targets not met. (63.5% achievement)

This is because the number of farmers benefitting from supply

chain services was only 37,000, much less than the set target

Level of satisfaction of services being offered and relationship with service

providers

84%

Farmers access better market for their milk

% of milk sold through DHs and formal channels

70% 93% 80% Exceeded by 13% (113% achievement)

Farmers have increased access to better supply chain

services

No of farmers who are active members and accessing improved supply

chain services

70% = 25,90045 42,203

Targets not met. (61.4% achievement) This is because the

number of farmers benefitting from supply chain services was only

37,000, much less than the set target

Level of satisfaction of

farmers on supply chain services and relationship

Price offered for milk-52%

Timeliness of milk collection-76% Timeliness of milk payment-72%

Qualitative

(Targets not set)

44 76% of total number of beneficiaries (37,000) 45 70% of total number of beneficiaries in the supply chain component (37,000)


49

Issue/ Level


Achievement

with DH or processor Information-78% Average-70%

Market System Change

Other DHs and processors improve their supply chain services to their

farmers (transport, timely payments, information, pricing,

etc.)

Perception of utility by non-target DHs/processors on providing quality services

to customers/farmers

Price offered for milk-38% Timeliness of milk collection-74% Timeliness of milk payment-66% Information-70%

Average-62%

Targets not set

DHs and processors have increased access

to retail markets of their products

No of DHs with increased access to retail markets

14 dairy hubs with 22,82846 registered farmers

8 DH and 3 processors

Targets exceeded (127% achievement)

Other DHs and processors crowd in and invest in improved customer-

centric distribution and retail strategies

No of non-target DHs and processors investing in improved customer-centric distribution and

retail strategies

3-Sogoo dairy Co-operative, Donyo Lesos Dairy Co-operative, Nabaya Cooperative and Nalepo Olepolos Dairy Cooperative)

Targets not set

DH and processors invest in improved customer-centric distribution and retail

strategies

No. of DHs and processors investing in improved customer-centric distribution and

retail strategies

5 dairy hubs - Limuru, Karate, Njabini, Gatamaiyu and Nyala. 2 processors, KCC and Lattana

16 DH, 5 processors

Targets not met (30% achievement)

No of target DHs and

processors renewing or continuing their investments in improved customer-centric

distribution and retail strategies after initial engagement

5 dairy hubs - Limuru, Karate, Njabini,

Gatamaiyu and Nyala. 2 processors, KCC and Lattana

16 DH, 5

processors

Targets not met

(30% achievement)

DH and processors No. of DHs/processors 5 dairy hubs - Limuru, Karate, Njabini, 16 DH, 5 Targets not met

46 Beneficiaries of KMT MAP


50

Issue/ Level


Achievement

invest in innovative products and/or new marketing strategies

targeting different customer segments

with new product innovations and/or marketing strategies


processors (30% achievement)

DH and processors

invest in energy efficiency upgrades through technology

firms

No. of DHs/processors

that have purchased new or additional energy efficiency solutions

5DHs and 1 processor 6 Targets met

(50% achievement)

DHs and processors negotiate agreements

with finance providers for debt, equity, and/or private

investment

No. of contract agreements signed

between DHs/processors and finance providers

3- Lattana Dairy Ltd, Lari and Limuru processors

6 DH, 1 processor

Targets not met (50% achievement)

DHs and processors invest in ICT

solutions for improved operational efficiency

No. of DHs/processors that purchased new or

additional ICT solutions

5 DHs and 2 processors 8 DH, 3 processors

Targets not met (63.6% achievement)

DHs and processors invest in improved supply chain

No. of DHs and processors investing in improved supply chain

management practices

5 dairy hubs - Limuru, Karate, Njabini, Gatamaiyu and Nyala. 2 processors, KCC and Lattana

20 Targets not met (40% achievement)

No. of target DHs and

processors renewing or continuing their investment in improved supply chain management

practices after initial engagement/contract

5 dairy hubs - Limuru, Karate, Njabini,


20 Targets not met

(40% achievement)

Dairy- Informal Supply Chain

Target Group

change

Other farmers increasing the use of

inputs and services through milk

No. of non-target farmers accessing inputs through

arrangements facilitated by the trader/village agents

AI-3% Hay-6%

Supplements and concentrates-5%

Targets not set


51

Issue/ Level


Achievement

traders/village agents

Farmers increase use of quality inputs and services through

traders/village agents

No. of farmers using quality inputs through arrangements facilitated by

the trader/village agents?

AI-6% Hay-7% Supplements and concentrates-6%

Average-6% Total=2,22047

38,640 Targets not met ( Need to revise this target)

(6% achievement)

Market

system change

Other traders increase

demand for quality milk from farmers and sale of milk to

processors

No. of non-target farmers

accessing inputs through arrangements facilitated by non-target traders (SCI)

Targets not

set

Other traders invest in branding, new

products and new retail models

No. of non-target traders adopting branding, new

products and new retail models (SCI)

2- All Seasons Milk Bar worked with a

brand and design firm (Renovazone) to redesign the milk outlets to meet the KDB and public health retail

certification requirements Prime Cut Ltd, a milk trader in Kisumu County has been able to

independently invest in product differentiation and a brand ‘moo’ targeting low income earners

Targets not set

Traders increase sales of milk due to improved supply chain

management

Litres of milk sold per day Prime Cut Ltd, a milk trader in Kisumu County increased daily milk intake from 50 litres per day to 2,000

litres per day

Targets not set

Dairy- Improving and Promoting AI Service

Target

group change

Farmers receive

higher price from the sales of the improved bulls and heifers

• Revenue generated in

Ksh annually from sale of improved bulls and

heifers

Heifers-

156,847,104 Bulls-

441,443,520

Other farmers % of non-target farmers 49.8% 57.8%

47 6% of beneficiaries of supply chain component (37,000)


52

Issue/ Level


Achievement

increase use of AI from well-rated AI providers

who used accredited AI for last AI service

Farmers increase use of AI from well-rated accredited AI

providers

Average price per insemination from internal/independent

service providers (Ksh)

1,523 • Local semen – Ksh. 1000;

• Exotic – Ksh. 1000-3000;

• Sexed- Ksh 6000-700048 Average- 1,578

1,316 Not achieved. – (84% achievement)

Proportion of farmers who used accredited AI for last AI service

35.3% 72.7% 55% Target exceeded by 18% (118% achievement)

Proportion of farmers purchasing breed services from an accredited AI

service provider

35.3% 72.7% 30% Target exceeded by 43% (143% achievement)

Proportion of farmers who "strongly agree" that

they are receiving affordable, quality, available, trustworthy

services from AI providers (average)

17% 35.8% 50% Target not achieved but there is an increase as

compared to the baseline (72% achievement)

Market

systems change

A.I providers offer

improved quality services

% change in insemination

success rate

75.4 79.2 85.4 Target not achieved

(93% achievement)

% change in awareness of existence of "accredited"

AI services by farmers.

75.9 75.4 Target exceeded by 0.5 (109% achievement)

Other AI providers

offer improved quality services

% change in insemination

success rate

68 Target not

set

% change in awareness of existence of "accredited"

AI services by farmers.

67 Target not set

AI providers have improved customer

service and business

No. of internal/ independent AI providers

investing in customer

• 7-Njabini, New Ngorika, Siongiroi, Nyala, Muki, Tulaga and Karate

200 Target exceeded

(109% achievement)

48 Source: Key informant interviews with Independent and Dairy Hub based AI providers


53

Issue/ Level


Achievement

skills to build farmer confidence in the service

service training from relevant market actors

• 218 AI service providers from 7 counties invested in customer

service training through the Continuous Professional Development (CPD) training

facilitated by LGS.

Other non-target AI providers have

improved customer service and business skills to build farmer

confidence in the service

No. of non-target internal/independent AI

providers investing in customer service training from relevant market

actors

By benchmarking with Siongiroi, 5 DHs in the Rift Valley (Chebusot,

Torongo, Naitiri, Lessos and Cherobu) have adopted several accreditation components.

Target not set

Internal/independent

providers invest in promotional and marketing activities

with relevant market actors

No. of internal and

independent providers investing with relevant market actors in

promotional and marketing activities

12 (Muki, Nyala, Karate, Kitiri, Tulaga,

New Ngorika, Njabini and Siongiroi, Caritas Embu, Githongo, Katheri and Buuri)

15 Target not achieved

(90% achievement)

Dairy-Commercial Hay Production

Target Group Change

Other farmers increase use of quality assured

fodder, both during the dry and rainy seasons

No. of non-target farmers accessing quality assured fodder, both during the dry and

rainy seasons

Rainy Season-56.5% Dry Season-87.8%

Targets not set

Farmers increase use of quality assured fodder,

both during the dry and rainy seasons

No. of farmers accessing quality assured fodder, both during the dry and rainy seasons

Rainy Season-61.5% Dry Season-93.2% Average-77.3%

Total = (24,083) 49

14,850 Targets exceeded by 71% (171% achievement)

Proportion of farmers claiming to purchase and use "very

good" quality hay

35.4 42.5 52.5 Targets not achieved by 10%

(90% achievement)

% of farmers who "strongly 15.7 28.5 25 Targets exceeded by

49 Proportion of farmers selling*Total beneficiaries feeds component (32,067)


54

Issue/ Level


Achievement

agree" on the satisfaction of price, quality and availability of hay; including future

investments in hay (Average)

3.5% (104% achievement)

Other CHPs sell more affordable

and high quality fodder

No. of non-target farmers selling more affordable and high

quality fodder

No data Targets not set

Other CHPs invest in soil testing

No. of Other CHPs investing in soil testing

0 Targets not set

CHPs investing in soil testing

What is the No. of CHPs investing in soil testing

2-Sochon, hay and forage 5 Targets not met (40% achievement)

CHPs invest in

co-branding of fodder

No. of CHPs investing with labs

in co-branding?

2

• Hay N Forage linked to Crop Nutrition Laboratory Services (Crop Nuts)

• Sochon worked with Agri-quest laboratories

2 Target achieved

(100% achievement)

CHPs access to working capital, asset-financing

and/or equity

No. of CHPs accessing working capital/assess financing and/or equity

Three financial institutions (Root Capital, Kenya Commercial Bank and Chase Bank) are providing debt

financing to commercial hay producers. A total of KES 26 Million has been extended to Hay N Forage

and Sochon towards business expansion, New KCC has secured an

undertaking of KES 800 million from Kenya Commercial Bank to pay for quality hay supplies.

2 Targets achieved (100% achievement)

Amount accessed by CHPs in working capital/asset financing and/or equity

Ksh 26 million Targets not set


55

5 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

5.1 Summary of Findings and Conclusions

The MAP program was designed to work in partnership with the private sector and

government to transform how dairy sector markets work so that they could become more

inclusive and more competitive. This is to be done by facilitating the realignment of the

incentives, capacities, relationships and rules which govern how markets work using the

"Making Markets Work for the Poor (M4P)" approach. This was to be done by interventions in

improving management and structures, improving AI services and promoting availability of

commercial hay to farmers.

Generally, the KMAP program has achieved a lot in terms of enhancing the supply chain

management and structures, improving AI services and promoting availability of commercial hay

to farmers so as to improve dairy sector markets so they become more inclusive and more

competitive and hence improve the production and incomes of small holder dairy farmers. This

is important bearing in mind that majority of the beneficiary farmers (66%) interviewed in the

relied on sale of milk as their main source of income.

The three years of implementation of the KMAP program 56,927farmers benefitted from the

program with a wide range of impacts. These impacts include, increase in incomes and

consequently building of farmers’ resilience to deal with social, economic and climate change

shocks. Overall, the average yield per cow per day for the two seasons stood at 8.9 litres

against a target of 10.7 litres, thus missing the set target by 1.8 litres. This is when compared to

the control group whose average was 7.8 litres per head. Since the average milked cows for the

treatment groups were 3, the average daily milk yields were 26.7 litres per household, with an

average price of Kshs. 36, this earned the household a total of Kshs.961 per day on average.

These further resulted to gross margins of Kshs 207 per cow per day during the wet season

and Kshs. 125 per cow per day during the dry season. This is lower than the recommended

gross margin of Ksh 267.1550. This implies that there is an opportunity of improving efficiency in

the dairy value chain to ensure that it is more profitable. KMAP has also enabled the famers to

gain from human capital benefits; increased knowledge and skills; social capital benefits; physical capital benefits; financial capital benefits; and natural capital as a result of engaging in dairy

farming.

Supply Chain

The farmers are also adopting new innovations promoted by KMAP thorough its implementers

in the private and public sector, as it was noted that the farmers practicing zero grazing grew

from 65.8% during the baseline to 69%. It was also notable that KMAP beneficiaries had

adopted pedigree and improved cows than the non-beneficiary farmers. This could be

50 Undated report: Findings of Moi University Agshare Pilot Project on Dairy Value Chain.

http://www.oerafrica.org/FTPFolder/Agshare/Marketing%20and%20Price%20Analysis/DairyFarmersResearchReport.pdf




56

attributable to capacity building under the project implementation as well as the ability of such

farmers to provide proper management through quality feeding and AI services.

Milk yields were also noted to be higher in the program beneficiary dairy hubs both during the

wet and dry seasons with averages of 9.7 litres and 8.0 litres during the wet and ry seasons

respectively. Further, the volumes of milk sold increased by 20% during the wet season and

40.2% during the dry season with the daily total volume of milk sold per household being 21.3

litres during the wet season and 16.8 litres during the dry season. This constituted 80% of the

total milk yields. Increase in volumes marketed was associated both with the increased yields

and access to more reliable markets.

The farmers selling milk through formal channels (Dairy co-operative/hubs and private

processors) increased from 70% in the baseline to 93%, implying that Dairy co-operatives/hub

were found to be the most preferred marketing channels mainly used to market milk by the

smallholder farmers. This is because the dairy hubs offer other integrated services from farmers

apart from buying milk including advance payments, feeds and AI on check off system and

extension services in some cases. In view, it is important to continue empowering the dairy

hubs to be viable business entities which would be instrumental in propelling smallholder

farmers from poverty.

Beneficiary farmers that had access to information and inputs were 76% (28,120 farmers).

Farmers’ accessing improved supply chain services was 70%, (25,900 famers). Bearing in mind

that the dairy hubs are the main provider of these services is a strong indication that farmers

are accessing better supply chain through improved distribution and retail channels. It was

however noted that the farmers were not satisfied with the prices they get for the milk the sell

and the prices they get from inputs such as AI, hay, feeds among others.

One of the interventions of the KMAP program is improving the informal supply chain services

by working with agents and traders. From the study, only 6% (1,927 farmers) accessed

information and inputs from traders and agents. It is therefore important to work with these traders and agents to ensure that the milk is handled in a safe manner and encourage them to

offer integrated services to attract more clients.

Increased professionalism in dairy enterprise management through improved supplier and retail

management is one of the key objectives of the KMAP program through efforts such as

investing in ICT solutions, investing in energy efficiency upgrades, adopting customer focused

marketing and distribution strategies, development of strategic plans and ensuring access to

finance. This is being implemented in partnership with the private sectors.

Adoption of ICT is important in improving the efficiency and effectiveness of the supply chain by

reduction of transaction and coordination costs. It was noted that very only five of the dairy

hubs had adopted ICT based systems to improve their operations. However, KMAP is making

efforts to develop user friendly ICT solutions to dairy hubs using partners such as iProcure that

develops systems that improve distribution of inputs and information. These systems are easily

adaptable to other dairy hubs even outside the KMAP intervention areas. A positive trend


57

noted is that three ICT firms have developed hotlines, authentication and rating platforms, and

12 agrovets are already using those platforms.

It was noted that 5 dairy hubs (Limuru, Karate, Njabini, Gatamaiyu and Nyala) and 2 processors,

(KCC and Lattana) were active in investing in improved customer-centric distribution and retail

strategies and in improved supply chain management practices. Some non-beneficiary dairy hubs

such as Sogoo dairy Co-operative, Donyo Lesos Dairy Co-operative, Nabaya Cooperative and

Nalepo Olepolos Dairy Cooperative also crowded in and invested in improved customer-

centric distribution and retail strategies.

It was notable that there is a lot of potential for the dairy hubs exposed to the interventions of

professional management companies continued to improve their business operations and thus

improving the performance of the dairy enterprises. Overall nine (9) of the intervention dairy

hubs had membership numbers growing by between 8% and 317%. Quantity of milk handled

also increased in seven (7) of the dairy hubs by between 11% and 94%. Turnover increased by

between 2% to 100% in eight (8) of the dairy hubs.

Other benefits reported by the dairy hubs included:

• Increased customers and sales;

• Improved stocks display and management;

• Improved access to financial resources;

• The dairy is now more focused on the growth path due to having strategic plan;

• Market research plan has enabled them to venture into new markets;

• Training on customer service helped reduce complaints from farmers and other clients

Five dairy enterprises were also linked with energy technology companies to work towards

creating energy efficiency solutions.

Improving and Promoting AI Service

Usage of accredited AI services has more than doubled from 35.3% in the baseline to 72.7% at

the midline. This may be attributed to KMAP’s strategy of leveraging the interconnectedness of

various market systems that are important to dairy, specifically breed improvement (through

provision of accredited AI services), feed, and veterinary services. The satisfaction levels with

the aspects of quality, availability and trustworthiness of the AI services were quite high. Those

farmers that were strongly satisfied with quality, availability and trustworthiness were 27.8%,

31.2% and 32.6% respectively as compared to 15%, 19% and 19% respectively in the baseline.

However, it is noteworthy that the farmers had a problem with the pricing of the AI services

because it is the aspect that exhibited the lowest satisfaction levels (14.6%). There is therefore

need to subsidize these costs so as to further increase the adoption of accredited AI services

and open up more space to more actors to provide accredited AI services.

Farmers complained that milk prices were lower yet Inputs costed higher. Milk prices ranged

from Kshs. 31.5 to Kshs 39.5 depending on the outlet. Prices for inputs such as A.I ranged from

Kshs 1,000 to Kshs 7,000 depending on the type (local, exotic, sexed etc) and source, while

prices of hay ranged from Kshs.250 to Kshs 450 depending on source, type of grass and season.

Different animal feeds had varying prices.


58

The main benefits the farmers got from the AI services include: increased milk production from

offsprings (77.5%); healthier calves (75.7%); high conception rates (68.6%); increased incomes

(63.2%). As a result of improved breeds, farmers earned a total of Ksh 163,732,957 from the

sale of improved heifers and bulls.

Efforts were made to ensure that AI service providers have improved customer service skills to

build farmer confidence in the AI service. This was done through various dairy hubs partnering

with private sector service providers, County Governments and LGSA to improve their

customer service skills. These include: Njabini, Nyala, Muki, Tulaga, Karate, Siongiroi,

(Chebusot, Torongo, Naitiri, Lessos and Cherobu)51. Accredited AI service providers reported

increased AI services provision by 61% between 2013 and 2016, implying an increase in number

of customers served.

AI service providers invested in promotional and marketing activities with relevant market

actors. Joint planning and financing was adopted as an efficient and effective model of delivering

quality services to small holder farmers. For instance, 8 DHs (Muki, Nyala, Karate, Kitiri, Tulaga,

New Ngorika, Njabini and Siongiroi) invested in joint promotional and marketing activities in

partnership with Twiga, Bimeda, Pokea, Indicus and Coopers. Five (5) dairy hubs (Nyala, Muki,

Karate, Njabini and Siongiroi partnered with two (2) processors (Brookside and Kinangop

Dairy) and five (5) genetics supply companies (Bimeda, Pokea, ABS, Twiga and Semex) for joint

AI promotional events.

Commercial Hay Production

Awareness of farmers on quality assured hay has increased from 9.1% at baseline to 41%. On

average 77% of the beneficiary farmers access quality assured hay during both the wet and dry

seasons. This implies that there is a high potential for purchase of additional hay products.

Eighty percent (80%) of the farmers indicated that they would be willing to purchase hay as long

as the quality is assured. This creates an opportunity for the private sector players can explore

business opportunities in hay preservation and marketing to make it available to small holder

farmers all year round.

The main benefits the farmers got from the use of quality assured hay include: increased milk

production (85.1%); increased incomes (67.2%); healthier cows (66.7%); higher quality milk

(55.2%); less variation in milk production (42.5%). The increased incomes enhance the dairy

farmers’ resilience to cope with social, economic and environmental shocks.

Through the project intervention, farmers were now able to access quality feeds such us quality

assured hay, which could be used during the dry season. This was made possible by linking

farmers with commercial hay producers, which meant that even without their own production;

farmers were able to source the feeds from the commercial producers. It was noted from the key informants that farmers used fodders from their farms and the surrounding during the wet

season. However, hay was good for dry seasons.

51 Benchmarked with Siongiroi


59

In addition to there been overall milk production per cow, it was noted that the difference

between milk produced across the seasons was still big at 1.7 litres. This would mean that

although KMAP interventions, and especially by availing hay for dry season feeding is being

undertaken, there is still work to be done to ensure that the seasonal fluctuations in milk

production are reduced. Proper feed budgeting throughout the year is therefore paramount to

ensure that farmers preserve fodder or have access to preserved fodder for them to purchase.

The availability of hay becomes handy to solve this challenge. Information gathered indicates

that only a handful of dairy hubs stocked hay in their feed stores, more so in the Central Kenya

region.

Efforts have been made to ensure that there is access to quality and affordable hay to

smallholder farmers through piloting with Hay N forage and Sochon to show the viability of

commercial hay farming. However, this is gaining acceptance because by in the last quarter of

2015 crossing over to the first quarter of 2016, the number of commercial hay producers has

increased to 28, with an equivalent rise in outreach numbers. KCC is also creating linkages

with Commercial Hay Producers to supply its customers with hay. County Governments are

also crowding in with Laikipia and Narok intending to fund hay production ventures. It was also

notable that about 55% of the intervention farmers procured their hay from their dairy hubs,

thus the need to enhance linkages between dairy hubs and hay produces to ensure that quality

assured hay is accessible to farmers in a timely and affordable manner.

Achievement of Targets

Overall, the program the achievement rate of the set targets was over 50% for most of the

program indicators including:

• Additional revenue from sale of milk -156% achievement;

• Additional volumes of milk sold in litres-105% achievement;

• Average yield per cow-110.3% achievement;

• No. of farmers accessing information and inputs-63.5% achievement;

• % of milk sold through DHs and formal channels-113% achievement;

• No of farmers who are active members and accessing improved supply chain services-

61.4% achievement;

• No of DHs with increased access to retail markets-127% achievement;

• No. of DHs/processors that have purchased new or additional energy efficiency

solutions -100% achievement;

• No. of contract agreements signed between DHs/processors and finance providers-50%

achievement;

• No. of DHs/processors that purchased new or additional ICT solutions-63.6%

achievement;

• Revenue generated in Ksh annually from sale of improved bulls and heifers- Heifers

(101% achievement); Bulls (57% achievement);

• Average price per insemination from internal/independent service providers - 84%

achievement;

• Proportion of farmers who used accredited AI- 118% achievement;

• Proportion of farmers purchasing breed services from an accredited AI service

provider-143% achievement;


60

• Proportion of farmers who "strongly agree" that they are receiving affordable, quality,

available, trustworthy services from AI providers-72% achievement;

• % change in insemination success rate-93% achievement;

• % change in awareness of existence of "accredited" AI services by farmers-109%

achievement;

• No. of internal/ independent AI providers investing in customer service training from

relevant market actors-109% achievement;

• No. of internal and independent providers investing with relevant market actors in

promotional and marketing activities-90% achievement;

• No. of farmers accessing quality assured fodder, both during the dry and rainy seasons-

171% achievement;

• Proportion of farmers claiming to purchase and use "very good" quality hay-90%

achievement;

• % of farmers who "strongly agree" on the satisfaction of price, quality and availability of

hay; including future investments in hay-104% achievement;

• No. of CHPs investing with labs in co-branding-100% achievement;

• No. of CHPs accessing working capital/assess financing and/or equity-100% achievement;

However, the following indicator targets had an achievement rate of less than 50%:

• No. of DHs and processors investing in improved customer-centric distribution and

retail strategies-30% achievement;

• No of target DHs and processors renewing or continuing their investments in improved

customer-centric distribution and retail strategies after initial engagement-30%

achievement;

• No. of DHs/processors with new product innovations and/or marketing strategies-30%;

• No. of DHs and processors investing in improved supply chain management practices-

40% achievement;

• No. of target DHs and processors renewing or continuing their investment in improved

supply chain management practices after initial engagement/contract-40% achievement;

• No. of CHPs investing in soil testing- 40% achievement;

5.2 Recommendations

The following recommendations are made based on the main findings and lessons learnt fro the

midline study.

a) The dairy hubs are the most important source of inputs and marketing channels for the

farmers. In view of tis, it is important for KMAP to continue empowering the dairy hubs

to be viable business entities which would be instrumental in propelling smallholder

farmers from poverty;

b) The use of key service providers for continuous professional development of dairy

hubs, milk traders, agrovets and other actors was noted as a good approach through

which challenges in supply chain management as well as delivery of A.I and animal health

services can be addressed. KMAP should therefore promote this approach to the wider

stakeholders in the dairy industry as a best practice and for wider impacts;


61

c) Benchmarking should be enhanced for dairy hubs and market actors that have adopted

and are implementing the program interventions well so that other actors may learn

from them. This may be done by developing case studies on the successful dairy

enterprises and organizing other dairy enterprises to learn from them.

d) The main challenges experienced by the AI providers are inadequate supplies, lack of

specialized semen handling facilities and equipment’s adnd facilities as well as perception

by farmers that AI services are expensive; and therefore slow uptake of A.I in some

areas. There is therefore need to provide assistance to the AI service providers to

aquire semen handling facilities and equipment as well as offering more training and

information on modern A.I services to dairy farmers focusing on the value proposition

for use of A.I services;

e) The study revealed that there were low numbers of DHs and processors investing in

improved customer-centric distribution and retail strategies. KMAP therefore need to

introduce new/innovative products and/or marketing strategies and improved supply

chain management practices so as to make greater impacts in the dairy sector;

f) KMAP in association with its partnerss should make efforts in enhancing appropriate

partnerships between the DHs, processors and private sector players as well as the

County Governments so as to improve on supply chain management, marketing and

retail strategies, as well as scaling up use of ICT to improve on the services provided to

farmers;

g) To ensure sustainability of the program interventions there should be concerted efforts

to involve the County Governments more intensely in the implementation of the KMAP

program. This is because implementation of livestock programs is a devolved function;

h) In order to effectively inform the program log frame, t is recommended that for future

evaluations, the same methodology applied in the midline assessment should be applied

in the end-line assessment to enhance comparability while the findings from the midline

assessment should be the basis for the setting the targets for the end-line evaluation.


62

6 APPENDICES

6.1 Appendix 1: Program Indicators

6.2 Appendix 2: Data Collection Tools

Double Click to Open

6.3 Appendix 3: DFID’s Livelihood sustainability framework

Dairy 2016 Indicators.zip


63

6.4 Appendix 3: List of Key Informants Interviewed

COUNTY DAIRY HUB TYPE KII NAME POSITION

Nyandarua Karati Treatment DH Official David Issy Njoroge Chairman

A.I Kiarie Mwangi Private

Agro vet Karati Agro Vet Manager

Njabini Treatment DH Official Peter Muhia Nduruhu Manager

A.I AI/Vet

Agro vet Peter Muhia Nduruhu Manager

Nyala Treatment DH Official Kelvin Ndegwa Dairy Manager

A.I DR. Richard Muraya A.I Provider

Agro vet Nyala Agro Vet Manager

CHP Nyala Hay & Forage Manager

Umoja Control DH Official David Macharia Manager

A.I Simon Maina Kanyua A.I

Agro vet Store Manager Store Manager

Nandarasi Control DH Official Julius Wahinya Kang'ethe Chairman

A.I Josphat K. Waithuki A.I

Agro vet Simon Maina Farmers Guide

Milk Trader Geoffrey Kinyua Milk Trader

Kahuru Control DH Official John M. Macharia Secretary

Kiambu Limuru Diaries Treatment DH Official Bidan Manager

A.I

Agro vet StoreStore supervisor

Gatamaiyu Treatment DH Official David Irungu Manager

A.I

Agro vet

Milk Trader Mary Njoroge (Mugunda)

Milk Trader

Kabete Control DH Official Morris Nzioka Manager

A.I Karanja Private

Agro vet Kabete DH Agro Vet Accountant

Embu Mkulima Bora Treatment DH Official Ms Florence Njeru Manager

A.I Dr. Opiyo-Farmlink Manager

Agro vet Dr. Opiyo-Farmlink Manager

Tumaini Treatment DH Official Leonard Nyaga Chairman

A.I Albert Gitonga Gichamu A.I Provider

Agro vet

Rugendo Treatment DH Official Henry Dickson Jomo Kagwithi

Chairman

A.I Augastine Mukundi Kariuki

A.I

Agro vet Dr. Opiyo-Farmlink Manager

Meru Processor Official Mr.Gitonga Production Manager

Feed manufacturer

Official Mr.Gitonga Production Manager

Bomet Singiroi Treatment DH Official Ann Bett Hub Manager

A.I Dr.Weldon Kirui A.I

Agro vet Singiroi Agro Vet Agro-vet Assistant


64

COUNTY DAIRY HUB TYPE KII NAME POSITION

Olbutto Control DH Official Vincent Langat Manager

A.I

Agro vet

Baringo Torongo Treatment DH Official Wilson Cheromei Assistant Manager

A.I John Kiprop A.I

Agro vet Robert/Lydia Clerks

Narok Ololulunga Treatment DH Official Winfred M Keiwa Chairman

A.I

Agro vet

Nakuru Technology Farm

CHP David Soisoi Director

Sochon CHP Director/Farm

Manager

Nairobi Dot Matrix BDS Willie Njoroge Director

Mac5 BDS Waweru Gichimu Managing

Consultant

PKF BDS Martin Muriithi Head of Agribusiness

Consulting

ConsumerPro BDS Jessica Kimathi Managing Director

Processors Processor Managing Director

6.5 Appendix 3: List of Documents Reviewed

i. Baseline assessment report on dairy hubs and their status in Kenya: November 2014

ii. Bebe, O.B., Udo, H.M.J., Rowlands, G.J., Thorpe, W., 2003 Smallholder dairying systems

in the Kenya highlands: cattle population dynamics under increasing intensification.

Livest. Prod. Sci. doi:S0301-6226(03)00013-7

iii. Dairy performance review rapid assessment report, March 2014

iv. Kenya Market Assistance Programme project closure report: Jan 2012 - Mar 2016

v. KMT Dairy intervention plans

vi. KMT Dairy sector evaluation baseline primary research report, Feb 2014

vii. KMT Dairy sector output reports: 2014-2015

viii. Review of MAP’s Dairy Sector Strategy, 2015-16: November 2014

ix. Undated report: Findings of Moi University Agshare Pilot Project on Dairy Value Chain.

http://www.oerafrica.org/FTPFolder/Agshare/Marketing%20and%20Price%20Analysis/Dai

ryFarmersResearchReport.pdf x. Staal, S., Pratt, A., & Jabbar, M. (2008). Dairy Development for the Resources Poor - Part 2:

Kenya and Ethiopia Dairy Development Case Studies. Rome, Italy: Pro-Poor Livestock Policy

Initiative. xi. Tegemeo Institute of Agricultural policy and development report on Productivity trends and

performance of dairy farming in Kenya, 2011




65

6.6 Appendix 4: Characteristics of the Treatment Dairy Hubs

N

o

Result Characteristics

Nyala

Nja

bin

i

Kara

ti

To

ron

go

Sio

ngir

oi

Tu

main

i

Lim

uru

Mku

lim

a

Bo

ra i

Ru

ge

nd

o

Olo

lolu

ng

a

Gata

maiy

u

1. Total Membership

2. 2012 10,000 _ 350 2957 8972 8,900 150 130

3. 2013 10,000 _ 500 2800 9690 9000 140

4. 2014 10,000 500 700 2845 10350 160 9500 80

5. 2015 11,963 610 750 2892 11228 160 9800 120

6. 2016 12,671 680 720 3037 12215 160 10365 625 208 140

7. Quantity of Milk in litres

8. 2012 624,301 1,545,270 Av150,000 4,353,227.99 7,594,815 1,080,000 759481

5

9. 2013 661,274 2,361,198 Av150,000 4,614,212.80 72,000 7,138,629 887,703 1,080,000 760,520 713862

9

10. 2014 659,032 378,637.5 3,964,275 Av150,000 4,641,531.80 360,000 6,666,655 1,588,480 1,080,000 980,340 666665

5

11. 2015 735,852 689,480 4,139,980 Av150,000 5,136,758.30 468,000 7,040,725 1,719,095 1,109,532 704072

5

12. 2016 577,917 618,192 4,049,572 Av150,000 5,136,758.30 7,155,656 1,007,776 720,000 1,111,820 715565

6

13. Turnover in Kshs

14. 2012 6,000,00 56,169,272 53,814,850 217,261,473 249,830,580 1,200,000

15. 2013 7,500,000 12,275,995.1 68,564,241 62,431,942 165,282,402 348,225,000 67,305,003 1,200,000 19,773,520

16. 2014 6,000,000 25,164,012.5 74,926,396 55,681,740.48 186,585,081 358,507,358 66,481,419 1,200,000 25,488,840

17. 2015 8,000,000 22,510,766.5 75,898,461 52,523,003.20 202,503,399 398,059,256 60,696,916 25,907,320

18. 2016 5,700,000 71,461,762 53,814,850 217,261,473 430,861,177 2,400,000

19. Where is the

milk sold?

Various

processors

direct sales to

residents

Brookside New KCC

Sun power

products

Brookside

dairies Ltd

KCC

Brookside

New KCC Sunflower

Products

Latanna

Pascha

Gwango

Yoghurt

Brookside

New KCC

Runyenjes

Superior

highlands

dairy

Brookside

30 milk

traders

20. Do you use

ICT

platforms?

Inuka Software

to manage

milk

recording,prep

rocess of

payments,

financial

reports

To manage milk

supplies and

payments

No

Uses Coop

works ICT

paltform

“da power’’

linking Milk

supply,Agrovet

and SACCO

Updating

records

Sending

messages to

farmers

Accounting

Financial

reporting

Not yet

embraced

technology

use.

21. Do you Quality AI About quality AI milk supply Information Milk quality Extension(Produ Information On milk Yes.


66

provide any

kind of

information

to farmers?

Source quality

feeds

concentrates

and

supplements

routine

training

payment

notification

service

sources of quality

feeds

right

concentrates and

supplements

statement,

rate of

payment

and inputs

deductions

AI

Information

Information

on feeds

on milk

quality.

Pasture/f

odder

management

Record

keeping

Financial

management

Disease &

pests control

Fodder

Establishment

Disease

Control

Insurance

information

Financial

information

ction & Milk

handling

Meeting

notification

Seminars

on quality

AI

Information

on sources

of quality

feeds

concentrat

es and

supplement

s

supply by

farmers

through milk

supply cards

Product

ion and

milk

quality

manage

ment.

22. Services

provided by

the HuB

Collecting milk

marketing

AI services.

Feeds and

concentrates

from our store.

Veterinary, drugs

and

agrochemicals.

Collecting

milk

Milk

marketing

AI services

Feeds and

concentrat

es supply

Supply of

inputs to

farmers on

credit

AI services

Milk advances

Training and

advisory

services

Loans through

Skyline

SACCO

Hardware

credit to

establish zero

grazing units

Hay supply to

farmers

Transport

services

AI services

Hay Provision

Feeds &

Supplements

Financial

services

NHIF,NSSF

Agro vet

services

Credit

facilities to

our active

members

only

Bulking of

milk

marketing

and

transport

services

Clinical & AI

services

Feeds &

Supplements on

credit

Food stuff on

credit

Extension

services

Linkages to

financial access

Mobile Money

Collecting

milk from

farmers

marketing

mainly to

New KCC

Collecting

milk

Marketing

Training of

farmers

Advise on

veterinary

drugs type

and use

Financial

literacy(Acce

ss to loans)

Check

of

payment

for

inputs

and

essential

food

stuffs

from

the farm

shop

23. Activities

undertaken

to improve

on Milk

Productivity

& Milk supply

chain

Hire transport

services

Train farmers

on how to

manage their

herd

Farm and herd

management

skills.

Hire transport

services on

behalf of farmers

Give feed and

supplements on

credit

Train farmers on

how to manage

their herd

Provide ICT

systems

Demonstration

to farmers

Offer

transport

services

Give feed

and

supplement

s on credit

Train

farmers on

how to

manage

their herd

Provide

Promoting of

zero grazing

Value addition

activities

(yoghurt

making)

Investing in a

cooling plant

(chilling Plant).

Educating

farmers during

field days on

quality and

increasing

quantity

Advocating AI

services

use(use of

motorbike for

Training on

importance &

use of AI

Training

farmers on use

and

management of

fodder

Training

farmers on use

and making of

silage

Price

information

Extension

Inputs on credit

Transport

Provision

of AI

services in

collaboratio

n with New

KCC

Extension

services

staff

Milk

transport

services

Farmer

trainings

Milk is

tested at

farm level

for quality

Training of

farmers on

milk hygiene,

handling and

storage/pres

ervation

Training of

farmers on

the types of

feeds that

can help

boost milk

productivity

Collecti

onqualit

y

control

Chilling

Transpo

rt


67

outreach)-

Subsidized AI

service

24. Activities

undertaken

to improve

on Milk

quality by

farmers

Hygienic Milk

handling

Train farmers

Using lactometer

to check milk

density.

Use graders who

ensure quality

before collecting

milk

Use of

Lactometer

(density

test and

Alcohol

gun test)

Graders

who

ensure

quality

before

collecting

milk

Practically

training

farmers on

how to ensure

and test milk

quality

Checking of

the quality of

milk by field

clerks at the

collection

centers

By strictly

ensuring that

milk is

collected and

handled in

aluminum cans

Training

farmers on milk

hygiene &

Handling

Providing

farmers with

Mazi milk

handling cans

Encouraging

farmers to

deliver milk on

time

Training of

our staff on

milk

handling and

hygiene

Ensure

quality

before

collecting

milk from

farmers

Milk stored

in cooler

after

receiving

from

farmers

Quality checks

for milk

Use

graders

who ensure

quality

before

collecting

milk

Delivering

of milk to

New KCC

chilling

plant

Test the

milk at

collectio

n

Chilling

milk to

maintain

quality

25. Activities

undertaken

to improve

on Skills of

farmers

Training of our

staff on milk

handling and

hygiene

Extension

trainings

Practically

training

farmers on

how to ensure

and test milk

quality

Educating

farmers during

field days on

quality and

increasing

quantity

Planning &

organizing

farmer field

days

Organizing

seminars for

farmers to

enhance their

skills

Exchange visits

Investing in

extension

service staff to

support

farmers

Training of

farmers on

milk

handling and

hygiene

26. Services/activ

ities

implemented

as a result of

participating

in the MAP

program

Training of staff

on milk handling

and hygiene

Mobilizing

farmers

Management of

Milk bar

preparation of

financial reports

Linking the Hub

with financiers

Internet program

Introduced A.I

providers such as

Training on

milk

handling

and hygiene

Operating

Milk bar

Preparation

of financial

reports

linking the

Hub with

financiers

Record

keeping for

Outreach

and

Collection of

Milk from

more

farmers


68

ABS and Pokes. financial

accounting

and feeds

stock

manageme

nt

27. Which

partners have

you worked

with?

Techno serve

Leowa

Associates

Inuka Software

-Brookside

-I procure-

-Kenya

Veterinary

Association

Techno

serve

Sunflower

products,

Twiga

Agricultural

Jubilee

Feeds

Limuru

Feeds Ltd

Mwanza

Feeds

Green

World

Feeds.

Bamscos

Kdff

Kcbf

PKF

Skyline

SACCO

KDFF-ICT

Coopers,

BIMEDA,

County

government

Boda boda

transporters

NEW KCC

AI providers

TNS (KPMG)

Co-op

consultancy

Land O’ Lakes

SNV PKF –

manage

ment

consulta

ncy

Farm

shop –

setting

it up

28. Which

partners did

you work

with to

improve

management

and efficiency

& roles

Leowa

Associates

Inuka Software

-Techno serve

-KAPAP

-I procure-

-KENAFF

USAIDs

Techno

serve

IFCD.

KDFF-ICT

KCBF-Kenya

commercial

bank

foundation-

Inputs

Ministry of

Agriculture

PKF-capacity

building

Bamscos

(Baringo

Farmers

Marketing

Cooperative

Society)-AI

service

provision

Coopers

Norbrook

KDFF

PKF MAK 5

Banks and

Microfinancial

institutions

AI-ABS, Twiga

chemicals,

Coopers,

BIMEDA,Count

y Government

Essential tracks

Ltd,

Pharma Inputs,

Vernah

Machineries,

LEWA

consultancy

on

ICT(SMS)

services

TNS (KPMG)

Land O’ Lakes

Co-op

consultancy

Kenya dairy

board

Techno

serve

County

Sacco

Feed

producers

Hay

farmers

Livestock

officers from

government

Priate AI

service

providers

ASDSP

TNS

(PKF) –

For

restruct

uring of

the

business

29. Impact have

these

activities had

in the dairy

hub

They have led

to increase in

milk

production

There is

increased

supply of milk

we receive

Increase in milk

production

Helped revive

the Cooperative.

Efficiency in

payment to

farmers

Increased supply

Increase in

milk

production

Improved

service

delivery to

farmers

The

cooperative has

established the

use of AI

champions paid

on commission

basis

Training of

The

Cooperative is

now more

focused on the

growth path

The cooperative

has measurable

goals to pursue

They have

led to

increase in

milk

production

Increased

supply of

milk to new

Improved

milk supply

by farmers

Efficient

transpo

rt

system

Quality

feed and

supplem

ents to


69

from our

farmers have

also increased

of milk to new

KCC and from

farmers

farmers on the

use and

importance of

AI services

Stocking

varieties of

semens to cater

for farmer

needs

The

Cooperative has

put in place in

market research

plan

Training

programs on

customer

service

KCC and

from

farmers

farmers

on

credit

30. What

changes have

you

experienced

as a result of

these

activities?

There is

increased

supply of milk

we receive

from our

farmers have

also increased

Improvement

on society

management

Adoption of

ICT

Increased

production

Increase of

active

membership

Improved

productivity

Improved

breeds

Improved

incomes(sales)

Improved

Membership

Employment

opportunities(A

gro vet staff, AI

champions)

Increase

d

member

ship

Increase

d milk

collectio

n

Reduce

d costs

of

operati

ons

from

efficient

milk

transpo

rt

system

33.

What

challenges

did you face

while

working with

MAP?

Poor feasibility

study before

implementing

the project

leading to

flopping of

most MAP

projects.

Involvement of

all

stakeholders

before

implementing

any project.

None

None Unsustainabilit

y-The project

is short lived

No challenge,

Feels the

project was

good as it

delivered on its

goals

The

program is

very new

to the Hub

so we

haven’t

experience

d any

challenges

thus far

Lack of

financials to

have an

greater

outreach to

farmers

Poor

transport

network to

transport

milk

The

manager

hasn’t

interact

ed with

TNS

since

march

2015

34. How would

these

challenges be

addressed?

Involvement of

all

stakeholders

before

implementing

any project

Reviving the

engagement

Engaging

More

partners

who are

reliable in

offering AI

None


70

More

extension

trainings

services and

sensitizing

farmers on

the

importance

of AI

services

35. Having learnt

from MAP

and its

partners,

have you

worked with

other new

partners

based on this

experience?

Training of

our staff on

milk handling

and hygiene

Mobilizing

farmers

preparation of

financial

reports

linking the

Hub with

financiers

Hay and

forage

department-

project failed.

Herd

management

program for

farmers

Not Yet Not yet Not yet

36. Are you

aware of

other hubs

around that

have

adopted/copi

ed some

activities or

engaged

partners

after learning

from MAP

Muki

Cooperative.

Not aware Had worked

with other

partners before

MAP and also

after MAP

Land O’ Lakes

Tumaini,

Mutugi,

Kirimiri


71

6.7 Appendix 5: Characteristics of the Control Group Dairy Hubs

No Result Characteristics

Kabete Olbutyo Umoja Nandarasi Kahuru

1. Total Membership

2012 2,189 441 90 -

2013 2,268 495 110 62

2014 2,311 526 120 124

2015 2,364 692 130 124

2016 2,367 984 756 134 124

2. Total Quantities handled in litres

2012 2,298,248 210,000 108,456

2013 2,510,604 300,000 126,356 48,000

2014 2,766,562 780,000 162,500 72,000

2015 3,685,909 950,000 197,390 78,000

2016 4,001,169 1,050,000 230,300 45,000

3. Total Turnover

2012 2298248 5,460,000 2,711,400

2013 2510604 8,100,000 3,411,612 1,302,000

2014 2766562 22,000,000 4,550,000 2,304,000

2015 3685909 27,550,000 5,921,700 2,496,000

2016 4001169 31,500,000 5,630,000 1,440,000

4. Where is the milk

sold?

Hubs owned by the

Kabete dairy hub

Milk traders

Selling chilled raw milk

processors (KCC)

Brookside

New KCC

Brookside Countryside

and New KCC

processors.

-Milk retail outlets

-Schools

-Hotels

Supply to SAMEER

Agriculture

Brookside chilling plant

Private buyers

5. Do you use ICT

platforms?

Captures data on milk

collected Processing

payments

Accounting, sales, stores

not yet embraced

technology use

Not yet embraced ICT The cooperative still uses

Manual system

No yet embraced technology(ICT)

6. Do you provide any

kind of information to

farmers?

Performance of the dairy

hub, farmer production

and milk quality issues,

prices, new development

in the dairy sector.

Price information

Quality requirements for

Milk

Information on AI

services, Information on

sources of quality feeds,

and the right

concentrates and

supplements for their

dairy cattle, fertilizer,

fodders planting, price

fluctuations, meetings

notification and milk

quality.

About quality AI, sources of

quality feeds, and the right

concentrates and

supplements for their dairy

cattle

Information on AI, Information on

sources of quality feeds, and right

concentrates and supplements for their

dairy cattle

7. Channels of Communication

route meetings, AGMs, Education meetings

Through farmer field days

AGM meetings

Through transporters

Extension officers, field days and radio

announcement

Extension, SMSs and telephone calls

Extension, field days, contact farmers and shows


72



SMS to farmers

8. How often is the

communication

AGM Once a year

Route meetings –

quarterly

Education meetings –

half yearly

Annually

Quarterly

When need arises

On need basis at least

twice in a month

On need basis When need for communicating to

farmers arises

9. Services provided by

the HuB

AI and clinical services on

check off

Feeds, livestock

supplements, equipment

on check off system

Extension services

Use of milk supplied to

guarantee bank advances

Collecting of milk from

farmers

Marketing of milk for

farmers

AI services

-Supply of feeds and

concentrates

AI services

Feeds and concentrates

AI services

Feeds and concentrates

Financial support through aberdares

Sacco

10. Activities undertaken

to improve on Milk

Productivity & Milk

supply chain

Collection, quality control

– chilling

Transport services

Training of farmers

Ensuring Quality of milk

and chilled storage

Transport services on

behalf of farmers

Supply feeds and

supplements on credit

Train farmers on good

farming practices

Supplying farmers with

quality feeds and

supplements

Trainings

Transport services

Supply feed and supplements on credit

Train farmers on how to manage their

herd, clean milk and animals health.


to improve on Milk

quality by farmers

Test the milk at collection

chill it to maintain quality

Conducting basic tests

on milk Density test,

Alcohol test during milk

collection

Training (Milk handling)

Use graders to test milk

quality at collection

points (Check is made

for Resasolin, Density

and butter fat contents.)

Graders test quality of milk

before collecting using

lactometer for density

checks and later at

processors plant.

Use of graders who ensure quality before

collecting milk from farmers using

lactometer, Alcohol test and smell.


to improve on Skills

of farmers

Farmer trainings in

collaboration with

stakeholders(Brookside,

KCC)

Extension trainings to

farmers

Trainings Offering training to farmers by extension

staff

13. Services/activities

implemented as a

result of participating

in the MAP program

Never participated in the

program

Not yet worked with

MAP program

Not yet worked with

MAP program

Not yet worked with MAP

program

Not yet worked with MAP program

14. Which partners have

you worked with?

KCC, Milk traders Brookside, KCC Milk processors,

transporters, Dairy

board, County

Government

Aberdares Sacco Brookside processor

County government

15. Which partners did

you work with to

improve management

and efficiency & roles

None None Milk processors,

transporters, Dairy

board, County

Government

Muki cooperative A.I

service provider and

County's government

Brookside processor

County government

16. Impact have these

activities had in the

dairy hub

17. What changes have

you experienced as a

result of these

None None None None


73



activities?

18.

What challenges did

you face while

working with MAP?

None None Not yet participated in

MAP program

None None

19. How would these

challenges be

addressed?

Not applicable Not applicable Not applicable Not applicable Not applicable

20. Having learnt from

MAP and its partners,

have you worked with

other new partners

based on this

experience?


program

Not yet worked with

MAP program

Never participated in

the program


program

Never participated in the program

21. Are you aware of

other hubs around

that have

adopted/copied some

activities or engaged

partners after

learning from MAP


program

Not yet worked with

MAP program

Never participated in

the program


program

Never participated in the program


74

6.8 Appendix 6: Characteristics of the Treatment Agro Vet Service Providers

No. Result Characteristics

Limuru Farm Link Siongiroi

1. What type of inputs do you sell in

your agrovet? Services provided by

the agrovet

• Animal feeds &

concentrates

• Supplements

• Chaff cutter

• milk cans

• Seeds

• Food stuffs

Fertilizers

-Seeds

-Animal feeds

-Animal concentrates and

supplements

-Agro chemicals

-Veterinary pharmaceuticals

AI services

-Laboratory

-Animal health

Advisory services on administering of drugs(de wormers) and supplements

Advisory services on Animal nutrition/feeding

Advisory services on importance and use of AI service

Advice on the general animal health(frequency of cow treatment and drug

use)

The Agrovet is newly established and has 2 employees (1 Male employee

and 1 female employee)

The Agro vet is connected to an ICT platform (da power) but has not yet

embraced its use.

2. Total Employees

3. 2012 6(M),7(F)

4. 2013 7(M),8(F)

5. 2014 7(M),8(F)

6. 2015 3 (2Male, 1 Female) 8(M),8(F)

7. 2016 3 (2Male, 1 Female) 8(M),8(F) 2 (1M) (1F)

8. Do you have an ICT platform for sales,

payments and information

management?

Only at the main office

Not yet, but planning to

install soon

Yes, Issuing receipts and

managing stock

9. What impact has the ICT platform

has in your business operation

Efficiency and speed

10. Challenges with the system Network failure

-maintenance cost is high

11. Do you provide any kind of

information to customers?

New products

Production and milk quality

Information about new

products and price changes

The Agro vet provides information to farmers on drug

application/use/dosage

General animal health

12. How/ what channels do you use?

AGM

Extension

Mobile phones and SMS Field days

Printing the message on the receipt of the proceed during delivery

Notice board on the cooperatives compound

The frequency of communication is on demand or when need arises

13. As part of the TNS/KMT initiative,

whom did you partner with (or are

working with) and what were (or are)

their roles?

TNS AGRI-Experience

MAK 5

Twiga Chemicals

BIMEDA

Norbrook

14. Do you use agents’ networks to reach

your customers and market your

products?

you provide

Yes, Has improved the sale and

services over the years

No

15. How did agents’ networks affect your

business performance?

Has improved customer

handling, satisfaction and

increased sales

Increase the flow of business

-Increases overall sales

No

16. Do you integrate customer service Helping customers load Provide information on The agrovets attendants always greets and warmly welcomes customers to


75


Limuru Farm Link Siongiroi

practice in your business operations? If

yes which kind of customer services

practice do you offer to your customers

products bought into their

cars, giving them extra

information, and general

customer courtesy

products and educate them on

the same

the agrovet

They too help customers load items on the motorbike/car

Enganging the customers and explaining to them on the various products

and their usage

17. What impact has the customer service

practice had in your business?

Customer satisfaction has

increased

Increased sales from even

non-members

Increase the flow of business

-Increases overall sales

This practice has made the Agro vet known-publicity

Improved sales

18. What services/activities have you

implemented (or you are currently

implementing) as a result of participating

in the initiative

VAN selling

Selling to smaller Agrovets

through the above method

Established and operating a model Agro vets

Stocking and selling of Hay

Selling of concentrates/supplements in bulk

Introduced and made use of ICT platform

19. In this TNS/KMT program, which

partners did you work (or are working

with) and what were (or are) their roles?

AGRI-Experience-branding and

marketing

EQUITY-loan facility

KDFF-ICT

MAK 5-Branding

Coopers-Input supply

Twiga Chemicals

Norbrook

BIMEDA

ALLURE

20. What changes have you experienced in

your agro vet as a result of these

activities/services

More sales/stocks Improved contacts with the farmers

Improved sales

Variety of stocks

Increased demand of agro vet inputs

21. What impacts have these activities had in

your agro vet?

Increase in number of

customers

Improved sales

More stock varieties

Improved customer service

Increased demand for agro vet products

22. What challenges have you encountered while in

this engagement

Interest rates on loan are high As a result of increased demand for agro vet products, farmers are

demanding inputs/products that are not stocked and when stocked they

never buy them.

The cooperative incurred high cost of establishing the agro vet which was

not in their budget

Delays in the construction work

Unavailability of construction of materials locally

23. Having learnt from the TNS/KMT engagement

and apart from partners introduced/ facilitated

by KMT/TNS, have you gone ahead and sought

to work with partners on your own initiative?

Please explain?

Other Agrovet dealer

-Farmer groups

24. If so, which activities have you implemented

though your own initiative?

Field days

-Association of Agro Dealer

25. Are you aware of other agro vets around

your area that have adopted or

duplicated some activities or engaged

partners/service providers after learning

from what you have implemented

YES-most Agrovets have learnt

from Farmlink


76

6.9 Appendix 7: Characteristics of the Treatment AI Service Providers


Limuru FarmLink CARITAS Embu Torongo FCS Siongiroi

26. Type of semen do you

mostly supply

AGRICS

WWS

AI total

Friesian

Arshire

Conventional semen (normal)

Sexed semen (genetic semen)

Friesian

Arshire

Semex

ABS

CRU

Viking geneticsics

27. Number of doses sold per

month on average

80-120 60 80 22 75-80

28. Average price per

insemination?

Local Shs. 1000

Exotic Kshs 1500-3000

Sexed Kshs. 7000

Min 1,000

Max 6,000

Kshs 1,000 Local-800

Imported-1500

1000

29. Challenges, faced with

regard to cost and

provision of A.I services

Farmers perceive the prices

to be high

Liquid nitrogen is

expensive

Validity of

semen(breakages,

expired

Competition

Cost variations

Debts from farmers

Some farmers still prefer the

bull due to lack of awareness

Loss of liquid nitrogen through

evaporation

Farmers complain of

high prices

Repeats

Inadequate supply of

liquid Nitrogen

Handling of liquid

Nitrogen

Lack of proper timing

information by farmers

High costs of repeat

cases

30. Changes in insemination

success rate over the last

one year?

Yes. Because if high viability

because of good storage

Yes as a result of farmers

are more aware of the

importance of AI services

60% in 2012 to 70-80% in 2016

mainly due to proper storage

and handling of semen

Dry spell uptake of AI

services has increased

Increased

31. Membership of Livestock

Genetics. Benefits for

membership

No

Not a member Yes. Joined in 2013 No Yes,2014

Exposure and

information about semen

distributors

32. Attendance of mandatory

annual Continuous

Professional Development

(CPD) training by LGSEA

and other industry

associations?

No Gets to know of the new

research findings

Acts as a refresher course

fresher course.

provide free services

around where the meeting

is taking place

Update news of Kenya

veterinary Board

-Continuous professional

development(CPD)

-Organizing Forum to meet and

network with other experts

Yes, on hoof health and

nutrition

Training on linking

farmers with AI

providers

Management of AI

Information by farmers

33. Do you use farmer groups

(Agent Network, AI

Champions, Farmer

Group Leaders) to

promote AI services?

Yes. To pass information to

other farmers

YES-

Mainly to reach the

farmers small Agrovets-eg

jamken, Murimi Mwega

YES-Through training of trainers

(TOT) where a group of about

10 farmers are invited in

CARITAS offices in Embu,

trained on AI technicalities and

trusted to go back to their

villages to train others

No Yes, AI champions,

34. Have the (Agent

Network, AI Champions,

Farmer Group Leaders)

helped improve number

of A.I services you provide

Yes. They recommend and

result to more clients

YES-This has improved AI

provision

YES-CARITAS as an

organization arrange for the

training which a requirement for

the Kenya veterinary Board in

order to renew the practicing

certificate

No Uptake of AI services

has increased

Members using AI

services have increased

Little cases of repeats

are reported

35. What has been the trend

of A.I services that you

2012=960

2013=1800

2012=600

2013=600

2012=500-600

2013=200-250

2012=50

2013=50

2012=240

2013=456


77


Limuru FarmLink CARITAS Embu Torongo FCS Siongiroi

have been providing to

farmers

2014=1800

2015=1440

2016=1440

2014=650

2015=720

2016=700

2014=200-250

2015=400-500

2016=

2014=60

2015=80

2016=120

2014=636

2015=720

2016=1020

36. Impact of customer

service practice in your

business?

Increased number of clients

due to referrals

Honesty in payment on

service of credits

Customer loyalty

Improved success rates (AI

services)

Enhanced service provision

Increased use of AI service

products

Increased number of

farmers reached

Increased turnovers

Increase in incomes

Increased milk supply

Increased number of

customers

Increase in volumes and

profits

37. When did you start

working with KMT/TNS

or their partners in the

MAP programme?

2015 2013 2012 2014

38. What major challenges

have you had while

working in the MAP

program

None The consistency of the

whole program is lacking

Distance-Trainers have to come

all the way to Embu

Communication costs(printing,

photocopies)

Additional costs which

were not budgeted for

Tight schedule in staff

training

39. Are you aware of A.I

service providers in your

area who have adopted or

duplicated some activities

or engaged partners after

learning from what you

have done in the MAP

program

Not aware There are there especially

those who have learnt

from us as the pioneers of

AI services and Agro vets

YES-about 4 AI providers No

40. Having learnt from the

MAP program and

working with partners

introduced/ facilitated by

KMT/TNS, have you gone

ahead and sought to work

with other market players

on your own initiative

AGRI-Experience

Other service providers eg

Agro vets Farmer groups

YES-We have worked with

other colleagues, farmers and

community at large

FICF-Finance innovation

for climate change fund

Climate smart

agriculture program

Heifer Pacific Project


78

6.10 Appendix 8: Characteristics of the Commercial Hay Providers


Hay N Forage Sochon Hay farm Technology farm

1. How many acres of land have you dedicated

to commercial hay production?

1,000 700 400

2. What is the number of times do you harvest

hay per year?

3 2 2

3. What was the production per acre in the

last season? (No. of bales produced?)

70 150 230

4. Who are your major customers for hay?

NYALA Dairy Cooperative Society Individual farmers spread across (Nakuru,

Baringo, Lessos, Kiambu,Machakos,

Kajiado,Kericho and Sotik

Cooperative societies through New KCC-In

Nyeri, Nyahururu & Embu

Farmers spread across areas of

(Kisumu,Kisii, Bomet,Mombasa

Kiambu/Nairobi,Eldoret,Nakuru and

Kericho)

Cooperative societies through New

KCC-In Nyeri, Nyahururu & Embu

5. What is the average weigh of the Hay bales?

(in kgs)

16 kgs 15 Kgs 15 Kgs

6. For how much do you sell hay? (KES)

Ksh.160 per bale Kshs 140 Kshs 195

7. What strategies do you use to ensure hay

produced is quality hay

Adequate fertilizer application

Proper weeding

Timely harvesting.

Early harvesting of hay (Flowering stage)

Ensure no weeds in the hay farm

Fencing the farm to keep away encroaching

livestock (grazing animals) to make sure

hay is free of ticks

Early harvesting of hay (Flowering

stage)30% flowering stage for cows and

40% flowering for seed multiplication

Ensure no weeds in the hay

farm(Weed Control)

Good land preparation-use of fertilizer

during planting and top dressing

8. What types of trainings have you received as

result of MAP in regard to hay production

and how useful were the trainings

Fire fighting

IT System

Techniques on Commercial Hay

production and value addition

Branding of Hay products

Fertilizer application

Record keeping

Data management

Importance of soil analysis and

interpretation

None as the farm was used to train

other Hay producers

9. How have your marketing activities about

hay changed since the engagement with

MAP?

Have increased Customers. Before engaging with the MAP project 80

percent of Hay was marketed through

brokers and only 20 percent by farmers.

Currently the trend has reversed and 80

percent of hay is marked through farmers

and 20 percent brokers

The director has also adopted value

addition of Hay by investing in the machine

to fine process hay and package before

marketing

No change noted in marketing of hay,

Manager argues that maybe it is

because the farm is well established

and the interventions helped only to

create networks with interested hay

farmers and not buyers


79


Hay N Forage Sochon Hay farm Technology farm

10. We are aware that the demand for quality

hay outweighs the supply, what are your

plans towards satisfying this demand?

Planting more Hay Establish stores at every station near

farmers to address seasonality of hay

supply(Embu, Narok, Kajiado and Nakuru)

Investing in more machinery to ensure

grass harvesting is timely

Addressing the storage of hay

11. As part of the MAP program, which input

providers have you been able to partner

with

John Deere Kenya seeds-Supply of grass seeds

YARA-Supply of fertilizer to the farm

Agri-quest-Soil analysis

None

12. As part of the MAP program, which financial

and equipment service providers have you

been able to partner with

Root Capital- Financial provider

John Deere-Equipment supplier

John Deer

Roots Capital

One source consultants-Business plan

development, financial management

The farm is well established with its

own Machinery and a good network of

input suppliers

13. What changes have you experienced in your

hay farming as a result of participating in the

MAP program?

Increased customers harvesting in time, that is at flowering stage

14. As part of the MAP program, which private

labs for co-branding and quality assurance

have you been able to partner with

Eco-media-for website development and

branding

15. Achieved changes

Increased Hay production

Establishment of out grower program under

the name Rift Valley Hay growers

Increased sales

Hay production has become popular in many

parts of the country unlike before when the

farm was starting

More awareness on the quality and standard by

farmers and consumers of hay as a result of

trainings

16. What challenges towards sustainable

production and commercialization of hay,

fodder do you face?

Drought,

Frost and

Theft

Storage challenges

Poor road networks

Financing of machinery not matching

productivity

Inability to make advance payments to

farmers for the hay supplied

Lack of enough storage facilities

17. What can the program do to reduce these

challenges?

Irrigation of grass, storage of Hay and fencing

of hay farms.

Capacity building in establishing the

association

Linkages of Hay producers with

transporters

Establish storage hubs in different areas


80

6.11 Appendix 9: Characteristics of the BDS Providers

No.


Dot Matrix MAC 5 PKF Consumer pro

1. Training modules

offered to dairy

sector

Brand creation

Customer service

Marketing

Advertising

PR

Shop remodeling

Agent network

Merchandising

Customer service

Team building

Financial and procurement

Governance

Investment

Business and strategic plans

HR

Customer service

Stock management

Customer service

Growth and expansion

Village agency

2. Feedback and

Response

mechanism on

customer

satisfaction

Exit mystery

shopper – To

monitor if

implementation is

being done

Random interviews

with shop customers

Post training follow ups

– To monitor if

implementation is being

done

Feedback from

beneficiaries

Track business performance of the client

Constant communication

Through donors e.g. KMT through their evaluations

Through evaluations

Exit interviews – revisiting what wasn’t

well understood

3. Methods of

training preferred

for the future

A combination of

the approaches

Simulation training –

puts participants into

their environments

Participatory approach

Simulation training – puts participants into their environments

Simulation training

4. Targeted changes

expected

Complete overhaul

of the feed shop

Improved sales

Improved customer

service

Shop remodeling

Rural sales strategies

developed

Improved customer

service

Increased membership/activation of dormant members

Better prices for farmers

Improved and efficient business performance

Quality feeds and other inputs

Improved customer service

Functioning feed shop

Increased membership and growing

business

5. Changes that the

BDS thinks

happened

The objective has

been achieved. Shop

sales increased

Better shop display

Good customer

service

Increased sales

Functioning rural

agents network

(cheque book system)

Shop remodeled

Increased member

loyalty

Increased membership and milk collection

Established farm shop

Efficient transport system

Lean organization – efficiently ran organization

Because of delays in starting the project

and internal management problems none

of the changes happened

6. Do you think it is

sustainable?

Yes.

Puts management of

change in the hands of

users/employees

Yes.

An efficiently ran business, offering better prices to members is a

viable business

She was pessimistic

7. Challenges faced

in implementation

of MAP

Delays from the

project

implementers.

Project took over a

year while could

have taken 3 months

Delays from the

project implementers.

Project took

unnecessarily long

period, some gains

were thus eroded or

not reached

The collapse of the partnership between TNS and KMT –

resulted to suspicion which slowed down project implementation

Mistrust at initial stages. Farmers and Hub management viewed

PKF as an audit firm pursuing issues related to taxes. This took

time to fade away

The skills of project implementers at the farm level were a

challenge for both PKF and KMT/TNS. Staff didn’t have proper

skills for lowest level implementation

Some BoD members were resistant to change and preferred

status quo because they benefited form the inefficient system. E.g

some members could not easily agree to reduce meetings as they

Internal management wrangles

The hub was perceived to have very low

capacity to be incorporated in the pilot

The project planning side was not well

organized. There were delays in

implementation of the project


81

No.


Dot Matrix MAC 5 PKF Consumer pro

were losing the sitting allowances

Some selected hubs had very low capacity thus a lot of effort was

needed to see results

8. Recommendations

for improvement

of the program

Reduce delay in

implementation

proper planning

TNS should have had a

budget for shop

remodeling

TNS should have

increased the budget

for the consultant –

they did more than the

resources could

support

TNS/KMT/PKF should have staff with proper skills and

experience for rural development

TNS KMT should have done a more thorough due diligence so as

to select hubs with considerable good capacity for the pilot

TNS KMT should have done a more

thorough due diligence so as to select hubs

with considerable good capacity for the

pilot

9. From Experience

with MAP has the

BDS sought to

work with other

partners

Had worked with

Agro-dealers before

Shop remodeling

was done in Limuru

for the first time and

turned out to be a

success. The BDS is

pursuing

opportunities to try

and do shop

remodeling with

other clients

Agency model is a new

model that is efficient

and effective.

Working with Agro-

vets in Makueni, Kitui

and Taita

Planning to do shop

remodeling for milk

traders

Yes.

Now working with Hubs in narok and a private dairy firm

Yes.

In talks with Sygenta to implement a

similar idea. But it not yet implemented


82

6.12 Appendix 10: AAER Model for Supply Chain Intervention

Nature of Intervention

Adopt Adapt Expand Respond

Increase professionalism

in dairy enterprise

management through

improved supplier and

retail management

• 5 dairy cooperatives (Mkulima Bora, Burungu, Tumaini, Rugendo and Gakundu dairies) in Embu County signed a

standard fee and performance based management contract with Sewa.

• 3 management firms (PKF, KPMG and Sewa) continued to develop innovations around management contracting in

order to improve their service offers to businesses. PKF introduced an agribusiness division fully dedicated to

agribusiness while Sewa developed more attractive fees offers (performance/ fixed) for business support. PKF also

works with New KCC, Kenya’s second largest milk processor, to provide services to over one hundred dairy

hubs supplying milk to the processor To date, 14 dairy hubs with 22,82852 registered farmer suppliers have benefitted from these turnaround services that include setting up and

restructuring the businesses to improve performance and growth.

• Three leading marketing firms – Dot Matrix, Consumer Pro and Mac 5 developed and promoted customer focused

marketing and distribution strategies for agrovets, piloted with three DHs (Limuru, Gatundu and Siongiroi).

• United States International University (USIU) has introduced an internship program for student consultants to support

roll out of village agent’s networks, for low cost up-to-date accessible extension services to farmers. USIU has

collaborated with Kenya’s Dairy Board to market its services countrywide. To date, 138 village agents have been recruited

by Agrovet input stores and input suppliers.

• 19 Agrovet input stores have adopted 17 improved retail and distribution practices for feed, breed and vet services

• 4 dairy equipment suppliers (ASHUT, Richie Technologies, ASL and Desley holdings) have developed a six-month

payment plan for acquisition of milk cans by milk traders registered under DTA.

• Upon exit of professional management firms, two (2)

DHs (Gatamaiyu, Limuru), have continued to improve

business operations. For instance, Gatamaiyu dairy has

continued the franchise arrangement with Farm Shop

for provision of inputs to farmers, installed a new

cooler and increased milk intake to 2000 litres per day

and undertaken restructuring to improve service delivery to

members. Limuru dairy on other hand has set up a

members SACCO.

• Limuru, Karate, Njabini and Nyala have continued to

implement the strategic plans they developed with the

assistance of KMAP.

• Limuru also went ahead to

engage Land O Lakes (LOL) to develop a marketing plan

based on the ideas gotten from the strategic plan.

• Prime Cut Ltd, a milk trader in Kisumu County has been

able to independently invest in product differentiation and a brand ‘moo’ targeting low

income earners. This trader has been able to realize

• 1 business, Mbiri Mwivoini Farm, a

private agribusiness

enterprise in Kirinyaga County

specializing in dairy and

horticulture, saw potential in

management services and

independently approached Sewa

and entered into a management

contracting service.

• MAC 5, a marketing firm is planning to do

shop remodeling for milk traders

• PKF has also expanded and is

also offering management

services to other dairy hubs that

are not in the KMAP in Narok

County (Sogoo dairy Co-

operative, Donyo

• 1 enterprise, Centum group of

companies independently

contracted PKF to undertake due

diligence and diagnostic study of

the poultry industry in Kenya to provide

insights into the business and

develop entry strategy

recommendation.

• Lattana Dairy, an SME processor in

Kiambu County currently processing

15,000 litres of fresh milk per day is

in the process of engaging PKF to

support setting up of new aggregation

centers and introduce

innovations leading to sustainable and

improved farmer loyalty and milk

quality. This is one of Lattana’s Dairy’s

expansion strategy

52 Beneficiaries of KMT MAP


83

• 1 ICT firm (virtual city developed a platform to host DTAs equipment payment plan through a structured bank

guarantee.

• 1 milk outlet (All Seasons Milk Bar) worked with a brand and

design firm (Renovazone) to redesign the milk outlets to meet the KDB and public health retail certification

requirements.

• I ICT firm, iProcure developed a new ICT based distribution

system for inputs and information. By aggregating demand through village agents who connect farmers to agrovets,

iProcure has been able to lower the cost of inputs and the distance farmers travel to access inputs and information,

thus making the offer attractive and relevant to them. Due to the benefits of the innovation there has been a significant increase in input procurement through the iProcure

platform, which is currently serving 21,893 customers.

• Some of the Treatment DHs had adopted ICT systems to

improve their operations.

• Five dairy enterprises with energy technology companies to

work towards creating energy efficiency solutions.

• KMAP has supported investments in dairy production and processing through debt and equity financing. These include

one dairy processor, Lattana Dairy Ltd., which raised expansion capital by ceding a minority stake to UK based

investors. KMAP also facilitated negotiations between potential equity investors and two dairy enterprises (Lari

and Limuru Processors) looking for equity investments. TechnoServe has also worked with businesses to prepare

them for equity investment.

improved business leading to:

II) increased daily milk intake from 50 litres per day to

2,000 litres per day and ii) business expansion by

opening up new dispensers in different locations in Mosoriot and Eldoret towns

in Uasin Gishu County and iii) setting up a collection center

aggregating milk from 40 small holder dairy farmers.

Lesos Dairy Co-

operative, Nabaya

Cooperative and Nalepo Olepolos

Dairy Cooperative)

and increased

appetite for quality milk.

• Sygenta, an agro-chemical manufacturer is in

talks with ConsumerPro to

provide marketing services to agro-

dealers

Source: Kenya Market Assistance Programme Project Closure Report: Jan 2012 - Mar 2016 and Midline Key Informant

Interviews


84

6.13 Appendix 11: AAER Model for Artificial Insemination Services Intervention



Service providers

have improved customer service

skills to build farmer confidence in the AI

service.

• Siongiroi engaged a marketing firm (Mak 5) to

train 6 inseminators in customer service skills. As a

result, the number of inseminations increased by

67% (from 250 to 375 compared to a similar period

last year.

• Njabini, New Ngorika,

Siongiroi and Karate are partnering with suppliers of

genetics to train their AI service providers in

customer.

• Njabini, Nyala, Muki, Tulaga and Karate continued partnering with

genetics suppliers7 to train their AI service providers in customer

service skills.

• One (1) dairy enterprise (Nyala)

has integrated customer service training for AI and animal health

service providers into its business operations.

• Olkalou and Miharati partnered with the county government of

Nyandarua to train 6 AI service providers in customer service

skills.

• By benchmarking with

Siongiroi, 5 DHs in the Rift Valley (Chebusot, Torongo,

Naitiri, Lessos and Cherobu) have adopted several

accreditation components.

• 218 AI service providers from

7 counties invested in customer service training through the

Continuous Professional Development (CPD) training facilitated by LGSEA.

• One genetics supplier (Indicus) is providing

customer service training as technical

assistance to its loyal customers. The

company is also developing a marketing

strategy to drive sales by building customer

service skills in all their accredited service

providers countrywide.

Service providers

invest in promotional and

marketing activities with relevant

market actors

• Siongiroi recruited 28 AI Champions to promote AI

services to farmers

• 8 DHs (Muki, Nyala, Karate,

Kitiri, Tulaga, New Ngorika, Njabini and Siongiroi)

invested in joint promotional and marketing activities in

partnership with Twiga, Bimeda, Pokea, Indicus and

Coopers.

• -Five (5) dairy hubs (Nyala, Muki, Karate, Njabini and Siongiroi

partnered with two (2) processors (Brookside and Kinangop Dairy)

and five (5) genetics supply companies (Bimeda, Pokea, ABS,

Twiga and Semex) for joint AI promotional events

• 4 DHs (Nyala, New Ngorika, Njabini and Siongiroi) have

established long term working relationships with 3 suppliers

(Bimeda, Pokea and Indicus) for promotional and marketing

activities.

• Caritas Embu was facilitated by the county government of

Embu to promote uptake of AI in the major dairy producing

zones of the county.

• Three (3) dairy hubs (Githongo,

Katheri and Buuri) partnered with Meru Central processor

for joint promotional activities to increase adoption of AI.

• The county government of Embu is facilitating

joint marketing events with coffee and Tea

cooperatives.

• New KCC Naivasha

partnering with four (4) DHs (Turasha,

Kinangop, Kirima and Umoja) for promotional

and marketing activities to increase milk

delivery.

Source: Kenya Market Assistance Programme Project Closure Report: Jan 2012 - Mar 2016 and Midline Key Informant Interviews


85

6.14 Appendix 12: AAER Model for Commercial Hay Production Intervention



Increase smallholder farmer

access to quality and affordable feeds

• Hay N Forage adopted commercial production of

quality hay in 1200 acres of land

• SOCHON Ltd. as an early adopter was able to expand

his acreage from 150 acres to 700acre.

• Afya millers have adopted a new feed formulation strategy which has seen their

sales increase by 25% in the month of June.

• Hay N Forage invested in equipment

for efficiency in the farm, employed staff

and set up a n office

• SOCHON invested in

a business plan, new equipment’s and

developed his brand

• Afya millers have invested in

Agricultural modeling and Training System

(ATMS) software to assist them in feed

formulations

• Due to drought Hay N Forage have

decided to identify another farm where

they can continue with hay farming.

• Through SOCHON initiative Rift valley hay

growers association has been formed with

a membership of 121 members producing

hay on 3300 acres of land.

• Afya millers have expanded its sales from 2 dairies to 13

dairies in three counties.

• Root Capital structured it’s financial packages and was able to loan Hay N forage Ksh 17

million for hay business

• Three financial institutions (Root Capital,

Kenya Commercial Bank and Chase Bank) are providing debt financing to commercial hay

producers. A total of KES 26 Million has been extended to Hay N Forage and Sochon

towards business expansion,

• New KCC has secured an undertaking of KES 800 million (GBP 575,539) from Kenya

Commercial Bank to pay for quality hay supplies.

• New KCC has established a feed platform for all commercial hay growers to supply hay to it’s

over 60,000 farmers.

• Kenya commercial Bank has taken an

undertaking with new KCC to fund dairies to facilitate purchase of hay.

• County governments such as Laikipia and Narok committed funds to support commercial hay production.

Source: Kenya Market Assistance Programme Project Closure Report: Jan 2012 - Mar 2016 and Midline Key Informant Interviews