household baseline survey report - … secretary ministry of agriculture, livestock and fisheries....

AGRICULTURAL SECTOR DEVELOPMENTSUPPORT PROGRAMME (ASDSP)

REPUBLIC OF KENYA

MINISTRY OF AGRICULTURE, LIVESTOCK AND FISHERIES

2014

Agricultural Sector Development Support Programme (ASDSP)

Ministry of Agriculture, Livestock and FisheriesHill Plaza, 6th Floor, P.O. Box 30028-00100 Nairobi

Tel/Fax: +254-20-2714867Email: [email protected]

www.asdsp.co.ke

LAIKIPIA COUNTY

Volume 1: HOUSEHOLD BASELINE SURVEY REPORT

University of Nairobi

Volume 1HOUSEHOLD BASELINE SURVEY

REPORTLAIKIPIA COUNTY

AGRICULTURAL SECTOR DEVELOPMENT SUPPORT PROGRAMME (ASDSP)


REPUBLIC OF KENYA


2014

© 2014, Government of Kenya

Agricultural Sector Development Support Programme (ASDSP)Ministry of Agriculture, Livestock and FisheriesHill Plaza, 6th Floor, P.O. Box 30028-00100 NairobiTel/Fax: +254-20-2714867Email: [email protected]

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FOREWORDAgricultural sector growth and development is crucial to Kenya’s overall economic and social development. In particular, agriculture significantly contributes to the national economy; ensures the country is food secure; generates incomes and provides employment both directly and indirectly for the population. Sustained agricultural growth is therefore critical to uplifting the standards of living of our people. The country however faces a number of challenges which need to be overcome for this growth to occur. These challenges include high levels of poverty, food insecurity and the negative effects of climate change.

Kenya’s development blue print, Vision 2030 recognizes the agricultural sector as one of the vehicles that will aid the achievements of the targets contained therein. Consequently, Agricultural Sector Development Strategy (ASDS) was put in place as a basis for formulating specific policies, work plans, projects and programmes that address food and nutrition security and farm productivity while conserving the natural resource base in the country. The overall goal of the strategy is to revolutionize agriculture from subsistence to an economic and commercial enterprise capable of providing Kenyans with employment opportunities and increased incomes. The government of Kenya in collaboration with other development partners and specifically with initial support from the government of Sweden has brought the realization of this goal a step closer through the Agricultural Sector Development Support Programme (ASDSP) at both the national and county governments’ levels.

In order to assess the status and impacts of this collaborative initiative, it was necessary to establish the existing realistic data on households, agribusiness and policy environment. Further, the two countries and stakeholders have committed to sharing information and data from time to time to chart the way forward in addressing the challenges that the sector faces in food security, productivity and natural resource management. This survey was therefore timely and critical for this nation as the basis for planning and setting priorities of intervention in the sector.

The survey has made pertinent observations in the counties that require urgent attention by both levels of governance and stakeholders. Of particular concern are the low levels of productivity and food security among households in most counties. Another area of interest realized was that the status of agribusiness though vibrant requires support in access to financial services if they are to compete favorably at international levels. As regards the policies and regulations governing the sector, they are in place and sufficient but there is lack of capacity both at national and county levels for their execution.

I wish to encourage all stakeholders to not only study the reports but also utilize the data and information for evaluating their activities and improving their implementation profiles to achieve realistic goals. As a ministry, we are committed to use the findings to inform the process of linking policy generated with future programmes that will lead to realization of food and nutritive secure, wealthy households.

Felix K. KoskeiCABINET SECRETARY MINISTRY OF AGRICULTURE, LIVESTOCK AND FISHERIES

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PREFACE

The Agricultural Sector Development Support Programme (ASDSP) was formulated by the government in 2011 in collaboration with development partners and other stakeholders to support the implementation of the strategies identified in the Agriculture Sector Development Strategy, ASDS (2010 – 2020) and the Comprehensive African Agricultural Development Programme (CAADP) Kenya Compact. The programme focuses on three key strategic areas; development of a transparent system for improved agricultural sector coordination and harmonization and creation of an enabling policy and institutional environment for the realization of the ASDS. Secondly, strengthening of the environmental resilience and social inclusion of Value Chains (VC) and finally promotion of viable and equitable commercialization of the agricultural sector through Value Chain Development (VCD). The baseline survey was a first step in the implementation of the programme with the sole purpose of generating real time data and information that will be used by programme implementers and other stakeholders to set benchmarks assess their performance and make adjustments to their implementation plans. The surveys had three main objectives;

First, to assess the socio-economic status of the communities especially the food security levels as this has an impact on cognitive human development which has an overall effect on the country’s economic growth. Other factors considered were their social inclusion, gender disparity and their resilience in adapting to environmental challenges. Results indicate that we still have a lot to do in order to increase productivity through increasing the community’s ability to access inputs and services.

Secondly, the objective of the agribusiness survey was to assess how actors along the value chains interact with one another and to determine how best to address their challenges. The survey reveals that the country has a vibrant agribusiness sector that can further be improved with the right agro trade policies and with both financial and technical support.

Lastly, to assess the current levels of policy formulation and institutional frameworks and to determine the gaps that may be hindering the advancement of agriculture. As can be seen from the results, we have formulated a number of policies to guide the sector. The capacity of our various institutions however, needs to be strengthened by adopting improved performance enhancing systems to deliver services more efficiently and effectively to our people.

We believe that this information will assist the national and county governments to improve on strategies geared towards food security and commercialization of agriculture in Kenya. We wish to acknowledge and appreciate the support of the Swedish government in the development of agriculture in the country and in particular for their commitment to the development of the sector through the ASDSP.

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The effective participation of MOALF staff, Kenya Agricultural and Livestock Research Organization (KALRO), the University of Nairobi (UoN), Kenya Institute of Public Policy Research and Analysis (KIPPRA) and the various collaborators is appreciated.

Sicily Kariuki (Mrs) MBSPRINCIPAL SECRETARYSTATE DEPARTMENT OF AGRICULTURE

Prof. Micheni Japhet Ntiba (PhD),CBSPRINCIPAL SECRETARYSTATE DEPARTMENT OF FISHERIES

Prof. Fred Sigor (PhD)PRINCIPAL SECRETARYSTATE DEPARTMENT OF LIVESTOCK

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ACKNOWLEDGEMENT The ASDSP is implemented at national and county level in the 47 counties through the established National Programme Secretariat (NPS) and the County Coordinating Units (CCU).

The purpose of the nationwide baseline surveys was thus to gather data and information to be used in establishing the pre-program levels of the result indicators contained in the Agricultural Sector Development Support Programme (ASDSP) log- frame. The surveys, which were intensive and costly were conducted between September and October 2013 covering the 47 counties in the country.

The specific objectives of the surveys were to; provide benchmarks and indicators for future evaluation of the program, provide useful data for planning and monitoring the progress made during implementation and mobilize various actors to participate in the programme interventions through the formation of partnerships at the critical stages identified by the survey data. Further, the study intended to avail data to be shared with other stakeholders to guide them in planning their activities.

Based on the functional lines, the survey was divided into three separate but complimentary segments that required different methods of data collection. The segments were household survey, focusing on resources, climate change and food security, agribusiness survey focusing on value chains, marketing and financial investments and lastly policy, institutional setting and coordination. I take this opportunity to extend special recognition and appreciation to the following, whose contribution led to the success of this exercise.

Mr. Felix K.Koskei, Cabinet Secretary, MoALF, for his leadership and support that enabled the completion of the study.

Sicily K. Kariuki (Mrs), MBS, Principal Secretary, State Department of Agriculture;

Prof. Micheni Japhet Ntiba, CBS, Principal Secretary State Department of Fisheries; and

Prof. Fred Sigor, Principal Secretary, State Department of Livestock for their guidance and support throughout the survey period.

The Hon Governors of the 47 counties for their leadership and support at the county levels.

The Embassy of Sweden under the leadership of Anders Ronquist for providing resources, support and guidance to the ASDSP.

The following for their technical and logistical engagement;Dr. Eliud Kireger, Director General KALRO, Dr. Ephraim Mukisira (former Director, KARI) and Dr. Joseph Mureithi for overall guidance and management of Household and Agribusiness surveys.

Dr. Lawrence Mose, Dr. Festus Mureithi and Dr.Wellington Mulinge for coordinating the Household and Agribusiness surveys.

Prof. Chris Ackello-Ogutu, University of Nairobi for the technical guidance in the household and agribusiness surveys.

The Central Planning and Project Unit (CPPU), MoALF under the coordination of Mr Wellington Lubira and KIPPRA under the guidance of Dr. John M. Omiti for carrying out the policy and institutional survey.

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The National Programme Steering Committee under the chairmanship of Ms Anne Onyango, MBS and Mr Julius Kiptarus, OGW, for guidance and oversight role.

The NIRAS Natura under the leadership of Mikael Segerros for supporting the whole process from the development of the baseline tools to its execution and analysis.

The National Programme Secretariat (NPS), for coordination of the baseline survey and in particular ASDSP M&E specialist, Rosemary Magambo for the day-to-day administration of the process.

The technical coordinating teams and the county staff, for their diligence and hard work that has seen the completion of the survey.

And to all those who contributed in one way or another towards this exercise, I thank you most sincerely for ensuring this report is produced.

Phoebe A. Odhiambo (Mrs) HSCNATIONAL PROGRAMME COORDINATORAGRICULTURAL SECTOR DEVELOPMENT SUPPORT PROGRAM

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TECHNICAL COORDINATING TEAM

Name Role Institution

Phoebe A. Odhiambo (Mrs) Survey Coordinator National Programme Coordinator ASDSP, MoALF

Rosemary Magambo Deputy Survey Coordinator

Monitoring and Evaluation Specialist, ASDSP, MoALF

Mikael Segerros Technical Assistance services

Consultant, Niras Natura,

Peter Shimon Technical Assistance Services

Consultant, Niras Natura,

Japheth Kiara Technical Assistance Services

Consultant, Niras Natura

Dr. Lawrence Mose Survey Team Leader Assistant Director,Planning, Monitoring and Evaluation, KARI

Dr. Festus Mureithi Deputy Survey Team Leader

Assistant Director, Socio-Economics and Applied Statistics, KARI

Prof. Chris Ackello-Ogutu Lead Technical Advisor

Professor Department of Agricultural Economics, University of Nairobi

Elias Thuranira Data Analyst Senior Research Officer, KARI

Alex Mwaniki Data Analyst Senior Economist, Statistician, MoALF,

Josephine Mogere Editorial Advisor Head Mass Media, AIRC, MoALF, Nairobi

Josiah Gitari Survey Supervisor KARI

David Mbugua Report Drafter KARI

Dr. Jane Wamuongo Report Editor MoLF

Dr.StellaMakhoha Editorial Review Senior Principal Research Officer, KARI

John Ayere ICT Support ICT officer, ASDSP

Jane Njeru Design Head Printing and Publications, AIRC Nairobi

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TABLE OF CONTENTSForeword ................................................................................................................................. i Preface .................................................................................................................................. iiiAcknowledgement ................................................................................................................ v Technical Coordinating Team .......................................................................................... viiList of Tables ........................................................................................................................ xiList of Figures ..................................................................................................................... xiiAbbreviations and Acronyms ........................................................................................ xiiiDefinition of Terms ............................................................................................................ xvExecutive Summary ......................................................................................................... xvii1.0 Introduction ..................................................................................................................... 1

1.1 County profile .................................................................................................................................................... 1 1.1.1 Location and size ................................................................................................................................... 1 1.1.2 Administrative and political units ......................................................................................................... 1 1.1.3 Demographic characteristics ................................................................................................................... 1 1.1.4 Land availability and use ........................................................................................................................ 2 1.2 Role of agriculture in the county ..................................................................................................................... 2

2. Rationale of the household baseline survey ............................................................... 5 3. Methodology ..................................................................................................................... 6

3.1 Approach .............................................................................................................................................................. 6 3.2 Sampling procedure ........................................................................................................................................... 7 3.3 Data collection and Analysis ............................................................................................................................ 7

4. Survey Results .................................................................................................................. 84.1 Household socio-economics and farm characteristics ................................................................ 8 4.1.1. Household size and gender ....................................................................................................................... 8 4.1.2 Level of Education .................................................................................................................................. 8 4.1.3 Primary occupation of households .......................................................................................................... 8 4.1.4 Household members with chronic illness, incapacitated or under social protection ................................ 9 4.1.5 Land ownership and access ................................................................................................................... 9 4.1.6. Land Tenure .............................................................................................................................................10 4.1.7 Allocation of land for different uses ...................................................................................................... 11 4.2 Production, use of inputs and decision-making in crops, livestock and fisheries .................. 12 4.2.1 Use of Labour in crop and livestock production .......................................................................... 12 4.2.2. Use of agricultural inputs, rates, costs and challenges ................................................................. 13 4.2.3 Use of purchased input in livestock production ................................................................................ 19 4.2.4 Decision-making in livestock production, by gender ........................................................................ 19 4.2.5 Constraints to using inputs in livestock .......................................................................................... 19 4.2.6 Use of machinery in farming activities .......................................................................................... 20 4.2.7. Input distribution networks and levels of satisfaction ................................................................... 21 4.2.8 Access to agricultural technologies ..................................................................................................... 25 4.3 Crop output and productivity ...................................................................................................... 25 4.3.1 Annual crops ........................................................................................................................................... 25 4.3.2 Productivity in perennial crops ............................................................................................................ 26 4.4 Marketing of outputs ...................................................................................................................... 26 4.4.1 Production and marketing of annual and perennial crops ............................................................... 26

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4.5 Productivity of different types of livestock ................................................................................. 26 4.5.1 Dairy productivity ................................................................................................................................. 26 4.5.2 Meat production ...................................................................................................................................... 27 4.5.3 Egg production ..................................................................................................................................... 28 4.5.4 Manure production ................................................................................................................................ 28 4.5.5 Apiculture production ........................................................................................................................ 29 4.5.6 Hides and skins production ................................................................................................................. 29 4.5.7 Decision-making on use of proceeds from sale of various livestock products ...................... 29

4.6 Contractual arrangements for marketing crops and livestock products ............................... 30 4.6.1 Contractual arrangements for crop marketing ..................................................................................... 30 4.6.2 Contractual arrangement for marketing livestock and livestock products ................................... 30

4.7 Value addition of crops and livestock products ........................................................................ 31 4.7.1 Value addition for different broad crop categories ....................................................................... 31 4.7.2 Value addition to livestock and fish products ................................................................................... 32

4.8 Employment and sources of household income......................................................................... 33 4.8.1 Farm income sources .......................................................................................................................... 33 4.8.1.1 Farm income sources ........................................................................................................................ 34 4.8.2 Income from off-farm and non-farm activities ................................................................................... 34

4.9 Poverty and Vulnerability .............................................................................................................. 35 4.9.1 Indicators of income and wealth ........................................................................................................ 35 4.9.2 Wealth and other socio-economic indicators by vulnerability ......................................................... 36 4.10 Food and nutrition security .......................................................................................................... 37 4.10.1 Food production, availability and seasonality ................................................................................ 37 4.10.2 Seasonality in food supply ................................................................................................................ 38 4.10.3 Food and nutrition security index ................................................................................................ 38 4.11 Collective action ............................................................................................................................. 40 4.11.1 Membership of household members to agricultural groups ............................................................ 40 4.11.2 Types and categories of groups ..................................................................................................... 41 4.11.3: Main commodities and activities of the groups .............................................................................. 42

4.12 Access and satisfaction with various services ........................................................................... 43 4.12.1 Access and satisfaction with support services and infrastructure ................................................ 43 4.12.2 Access, use and satisfaction with credit ............................................................................................ 43 4.12.3 Access and satisfaction with market information .................................................................... 44 4.12.4 Access, use and satisfaction with formal savings services .............................................................. 44 4.12.5 Access, use and satisfaction with insurance services ....................................................................... 44

4.13 Climate change challenges, adaptation and coping strategies .............................................. 44 4.13.1 Sources of climate related information ......................................................................................... 44 4.13.3 Types of adaptation strategies to climate change ............................................................................ 45 4.13.4 Training on climate change strategies ............................................................................................... 45 4.13.5 Types of climate shocks experienced ................................................................................................. 46 4.13.6 Coping strategies to climate change ................................................................................................. 47 4.14 Natural Resource Management practices ................................................................................... 48 4.14.1 Proportion using agroforestry and types of agroforestry practices used ........................... 49 4.14.2 Main natural resource management practices known and used ................................................... 49

5. Conclusion and Recommendations ............................................................................. 50

6. Bibliography .................................................................................................................... 51

7. Annexes ............................................................................................................................ 52Annex 1: ASDSP logical framework - County Baseline Indicators .............................................. 52Annex 2: Respondents owning different household assets in the County (%) ............................ 58

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LIST OF TABLESTable 1.1: Administrative and political units, Laikipia County ...................................................................... 1 Table 1.2: Crop production in the County ......................................................................................................... 3 Table 1.3: Quantity and value of livestock and livestock products ................................................................ 3 Table 4.1: Distribution of levels of education attained by household head ................................................. 8 Table 4.2: Primary occupation of household heads ........................................................................................ 9 Table 4.3: Ownership of different parcels of land accessed by the households ......................................... 10 Table 4.4: Access to different parcels of land by household members ......................................................... 10 Table 4.5: Proportion of land tenure system by gender ................................................................................. 11 Table 4.6: Land tenure system for different parcels of land owned by the household ............................. 11 Table 4.7: Land allocated to different uses by different household types ................................................... 12 Table 4.8: Household use, disaggregated by gender, of various inputs in annual crops during Season 1 ...... 13 Table 4.9: Decision making in production of annual crops, by gender ........................................................ 14 Table 4.10: Household use of various inputs in annual crops during Season 2 ......................................... 15 Table 4.11: Level of input use at farm level for annual crops by gender ..................................................... 16 Table 4.12: Level input use for perennial crops at farm level, by gender .................................................... 17 Table 4.13: Decision-making in annual crop production during Season 2 ................................................. 17 Table 4.14: Households using various inputs in perennial crop production ............................................. 17 Table 4.15: Decision-making in perennial crop production by gender ........................................................ 18 Table 4.16: Households who encountered major constraints in input use .................................................. 18 Table 4.17: Households (by gender) using various inputs in Livestock production ................................. 19 Table 4.18: Decision-making on livestock production for different livestock types ................................. 19 Table 4.19: Major constraint on using inputs for livestock production ....................................................... 20 Table 4.20: Type and source of machinery/equipment ................................................................................... 20 Table 4.21: Access to agriculture-related services and infrastructure .......................................................... 22 Table 4.22: Households accessing services, by gender of household head ................................................ 23 Table 4.23: Households satisfied with services .................................................................................................. 23 Table 4.24: Households accessing agricultural technologies ............................................................................ 24 Table 4.25: Main crop grown in Season 1 .......................................................................................................... 24 Table 4.26: Main crop in Season 2 ...................................................................................................................... 25 Table 4.28: Proportion (%) of crop produce (annual and perennial) marketed by households ............... 26 Table 4.29: Average milk production of different dairy animals during the dry season .......................... 27 Table 4.30: Milk production by different dairy animals during the wet season ......................................... 27 Table 4.31: Average meat production for different meat animals, by household head ............................ 28 Table 4.32: Egg production for different types of poultry ............................................................................. 28 Table 4.34: Decision-makers on use of proceeds from sale of milk and eggs ............................................. 29 Table 4.35: Sale of livestock products ............................................................................................................... 30 Table 4.36: Households with contractual arrangements for sale of livestock and livestock products .... 31 Table 4.37: Value addition by type of household head ................................................................................... 31 Table 4.38: Value addition by categories of livestock and fish products .................................................... 32 Table 4.39: Average annual household income from on-farm activities, by gender .................................. 33 Table 4.40: Average annual household income from off-farm and non-farm activities ............................ 34 Table 4.41: Average off-farm household income ............................................................................................. 34 Table 4.42: Mean values of various indicators of income and wealth, by gender of household head ..... 36 Table 4.43: Wealth and other socio-economic indicators by vulnerability .................................................. 37 Table 4.44: Peak and low season food availability in the county .................................................................. 38

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LIST OF FIGURESFigure 3.1: Map of Laikipia County showing the sampled households locations ............................... 7 Figure 4.1: Contribution (%) of labour in crop and livestock production, by gender .......................... 12 Figure 4.2: Households using different seed types during Season 1 .................................................... 13 Figure 4.3: Households use of different seed types during Season 2 .................................................... 14 Figure 4.4: Main constraints in the use for major crop production ....................................................... 18 Figure 4.5: Activities mechanized .............................................................................................................. 21 Figure 4.6: Main sources of machinery ..................................................................................................... 21 Figure 4.7: Propotion (%) of households accessing different services ................................................. 22 Figure 4.8: Access to financial services by household head, by gender ................................................ 24 Figure 4.9: Actors involved in crop sale contractual arrangements ...................................................... 30 Figure 4.10: Income sources available to households ............................................................................. 33 Figure 4.11: Proportion of households that were food insecure over the 12-months of the study period ........ 38 Figure 4.12: Membership to groups by household members ................................................................ 41 Figure 4.13: Types of agricultural group members belonged ................................................................ 41 Figure 4.14: Category of the groups ........................................................................................................... 42 Figure 4.15: The main commodities the groups dealt with .................................................................... 42 Figure 4.16: Access to support services ..................................................................................................... 43 Figure 4.17 Household members who accessed savings services ......................................................... 44 Figure 4.18: Sources of climate related information ............................................................................... 44 Figure 4.19: Climate shocks experienced ................................................................................................... 46

Table 4.45: Mean of dietary diversity score/index ........................................................................................... 39 Table 4.46: Distribution of respondents by dietary diversity score/index ................................................... 39 Table 4.48: Distribution of food secure and insecure households ............................................................... 40 Table 4.49: Level of satisfaction (%) with market information services ....................................................... 43 Table 4.50: Awareness of households about long term environmental changes %) ................................... 45 Table 4.51: Strategies for adapting to climate change ..................................................................................... 45 Table 4.52: Household members trained in adaptation to climate change ................................................. 46 Table 4.53: Coping strategies to climate change .............................................................................................. 47 Table 4.54: Response to climate related shocks ................................................................................................ 47 Table 4.55: Capacity of households to respond to the climate shocks) ........................................................ 48 Table 4.56: Households practising agroforestry technologies ....................................................................... 49 Table 4.57: Knowledge and practices of natural resource management ..................................................... 49

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ABBREVIATIONS AND ACRONYMS AI Artificial Insemination

APVC Agricultural Prioritized Value Chains

ASDS Agricultural Sector Development Strategy, 2010-2020

ASDSP Agricultural Sector Development Support Programme

CAADP Comprehensive African Agricultural Development Programme

CC Climate Change

CIDP County Integrated Development Profile

CRA Commission for Revenue Allocation

FAO Food and Agriculture Organisation of the United Nations

FBO Faith Based Organization

FGD Focus Group Discussion

FHH Female Headed Household

FHM Female Headed Managed

GIS Geographical Information System

GoK Government of Kenya

GPS Geographic Positioning System

HH Household Head

KALRO Kenya Agricultural and Livestock Research Organization

KARI Kenya Agricultural Research Institute

Kcal Kilo calories

KCC Kenya Cooperative Creameries

KDB Kenya Dairy Board

KENAF Kenya National Association of Farmers

KENFAP Kenya Federation of Agricultural Producers

KES Kenya Shillings

Kg Kilogramme

KIPPRA Kenya Institute for Public Policy Research and Analysis

KPHC Kenya Population and Housing Census

KRA Kenya Revenue Authority

LMIS Livestock Information Management Systems

M&E Monitoring and Evaluation

MDGs Millenium Development Goals

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MHH Male Headed Household

MOA Ministry of Agriculture

MOALF Ministry of Agriculture, Livestock and Fisheries

MOLD Ministry of Livestock Development

NGOs Non-Governmental Organizations

NPS National Programme secretariat

NRM Natural Resource Management

PCP Per Capita Production

PPS Proportionate to Population Size

RATIN Regional Agricultural Trade Intelligence Network

SACCO Savings and Credit Co-operative Organization

SE Standard Error

SIDA Sweden through Swedish international Development Agency

SIMULESA Sustainable Intensification of Maize-Legume Cropping Systems for food security in Eastern and Southern Africa

SPSS Statistical Package for Social Sciences

TC Tissue Culture

WFP World Food Programme

YHH Youth Headed Household

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DEFINITION OF TERMSBaseline survey: Refers to the analysis of the current situation to identify the starting point for a project or programme. It is a collection of primary and secondary data which describes the situation at a particular time. It is conducted within the framework of a proposed development intervention; in this case the ASDSP programme

County: Refers to one of the 47 devolved administrative/political units in Kenya.

Dietary Diversity Index: refers to a figure obtained by assessing the average number of food categories (out of a total of 12 broad food categories) a household consumed in the past one week prior to the survey. Households that consumed from a maximum of two food groups/categories were considered as having low food diversity while those who consumed from a minimum of three food groups/categories were considered as having high food diversity.

Farm income: Refers to income that a household derives from sources within its farm(s) Examples include income from crops and livestock; income from use of farm machinery eg hiring out of tractors and income from use of posho mill etc;

Female-Headed Household: This is a household whose main decision maker on agricultural production, marketing and consumption is a female person aged 36 years and above.

Food Security according to FAO (1996) refers to “all people, at all times, having physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life”. A food-secure household was defined as one whose calorie supply per Adult Equivalent is greater than or equal to the minimum daily calorie requirement of 2,260 kcal for an adult.

Gender: Categorizes people in terms of their roles and responsibilities as provided by the social and customary considerations of a given society. Gender does not refer to sex of an individual. For this study, four gender categories were used: Adult males (men aged 36 years and above); Adult females (women aged 36 years and above.), Male youth (men aged 18-35 years of age), female youth (women aged 18-35 years of age).

Household: This is a collection of persons who depend on a common store. The persons may not necessarily be members of the same family. They often make common production, marketing and consumption decisions.

Land Parcel 1: Refers to the parcel of farm land where the homestead is located.

Land Parcels 2 & 3: These were any other two parcels of land owned by the household that were not contiguous with the homestead. On average, most households had more than one parcel of land. For this study, the maximum number of land parcels were limited to three.

Livestock Off-take: Value of livestock that a household sells or liquidates in one year in order to maintain the herd / flock size and / or meet financial obligations.

Logical Framework: This is a management tool used for designing, monitoring and evaluating development projects/programmes

Male-Headed Household: This is a household whose main decision maker on agricultural production, marketing and consumption is a male person aged 36 years and above.

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Non-farm Income: Refers to income derived from other sources apart from farming activities by household members. Examples include salaried employment, business, etc.

Nyirinyiri : This is meat that is dried /fried and preserved by deep frying in fat to enhance its shelf-life. It is commonly found in the dry regions of the country.

Off-farm Income: Refers to income derived from farming activities undertaken outside the household farm setting. The activities could be farming or non-farming in nature. Examples include farm wage labour, marketing of produce that is not of the household..

Primary occupation: The main activity from which the heads of household derive their livelihood and income.

Productivity: This is production per unit of resource. The term is applied to crop / livestock production per unit of land or animal (yield) within a specified time period (day, season or year) in this study.

Seasons one and two: This is a specific reference made for purposes of this study with respect to the time the data was collected (September and October 2013). Season One: Refers to the cropping season that spaned from August 2012 to February 2013. Season Two: Refers to the cropping season that spaned from March 2013 to August / September 2013.

Social protection: Refers to affirmative action taken by Government, Development Partners or other agencies to assist the vulnerable such as the elderly, incapacitated and those with terminal diseases, food insecure ,poor to cushion them from livelihood challenges they face.

Technology: This refers to a process or technique that enhances crop or livestock productivity. Examples include use of improved seed, fertilizer etc

Value addition: Refers to any activity or process that enhances the value of a product through a number of ways; by increasing its shelf-life or improving accessibility / ease of sale of product, etc with or without transforming the original product.

Vulnerability: Is a state of inadequacy. A household member is vulnerable when he/she lacks adequate resources to meet their basic human wants: food, shelter, clothing etc.

Youth-Headed Household: This is a household whose main decision maker on agricultural production, marketing and consumption is a male or female person aged between 18 and 35 years irrespective of sex or marital status.

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EXECUTIVE SUMMARYThis report presents the results of the household baseline survey carried out in Laikipia County to establish the pre-Programme status (or levels) for result indicators in the Agricultural Sector Development Support Programme (ASDSP) log-frame (Annex 1). The Programme, initiated 2012, is a sector-wide Programme implemented by the GoK in collaboration with Development Partners and other stakeholders in the agricultural sector. It is aligned to the Agricultural Sector Development Strategy (ASDS) and the Comprehensive African Agricultural Development Programme (CAADP) Kenya Compact. Furthermore the ASDSP contributes to the realization of Kenya’s wider development goals as expressed in the Millennium Development Goals (MDGs), Vision 2030 and the Kenya Constitution (2010). The Current 5 year phase (2012-2016) is prepared by the Government of Kenya with the assistance of the Government of Sweden, through the Swedish International Development Agency (SIDA).

The main purpose of ASDSP is to increase equitable income, employment and improved food security of male and female target groups as a result of improved production and productivity in the rural smallholder farm and off-farm sectors. The Programme has defined its results indicators in the program log-frame which require baseline levels as part of the Programme implementation strategy. A nationwide survey to establish the status of baseline levels was conducted between September and early October 2013 covering the 47 counties in the country. The survey was carried out by the Ministry of Agriculture Livestock and Fisheries (MoALF) through the ASDSP in collaboration with Kenya Agricultural Research Institute (KARI) and the University of Nairobi (UoN). A sample size of 161 households of which male, female and youth headed households made up 67%, 22% and 11% respectively was determined. The sample was determined using the proportionate to population size (PPS) technique and other parameters. Data collected was analyzed using the Statistical Package for Social Sciences (SPSS) software.

The results revealed that a typical household had an average of four members. The mean size of MHH was five while the FHHs and YHHs had mean size of three and four members respectively. About 68% of all the household heads had attained upper primary and secondary school of education. Household heads with certificate/vocational level of education and above were only 2%.

Overall, about 60% of the county population derived their livelihood from agriculture which contributes 75% of household incomes. Crops grown included maize, beans, Irish potato, wheat, cabbages and tomatos. Main types of livestock kept included cattle, sheep, goats, camel, donkeys, poultry and pig. Fish farming was gaining prominence after its promotion by the ESP. Income from crops was KES 2.5 billion with KES 1.7 million coming from maize (dry). Livestock contributed KES 705 Million with beef and mutton contributing KES 294 and 270 Million respectively.

Almost all households (93%) had one source of income. Main sources of household income were on-farm activities, salaried employment, on-farm and off-farm wages, businesses and remittances. On-farm income earned the households an average of KES 204,370 with crop sources contributing the largest portion of this income (KES 200,142) compared to livestock activities that contributed KES 140,677. The mean value of total household income for Laikipia County was about KES 286,888 while the mean value of gross wealth was about KES 1,583,817 Million. The annual per capita income was KES 71,722 and per capita gross wealth KES 395,954. Mean daily per capita income was KES 197, with daily per capita income for MHHs and YHHs being higher (KES 190) than FHHs of (KES 108).

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Family labour in crops production was mainly provided by adult females (49%) and the youth (37%) while hired labour was provided mainly by adult females (58%) and adult males (28%). Family labour in livestock production was provided by all family members while hired labour in livestock production was provided predominantly by the youth. In Season 2, the use of improved varieties of maize and tomato was the highest at 65% and over 80% respectively while it was lowest in beans and Irish potato at 5% and 4% respectively. Between 50% and 58% of respondents used local varieties of beans and Irish potato. Seed/planting material had the highest adoption rates (73% of the households) while the rest of the inputs had less than 20% of the households using it. Adult males dominated in making decisions for market oriented crops such as maize and tomato while adult females dominated in food crops such as Irish potato and beans. For Season 1 apart from the seed/planting material that had 28% of the households, the rest of the inputs had less than 15% adoption rates. About 5% of the households who grew perennial crops used organic manure. The major constraints encountered across all inputs were high prices (38%) and long distance to input markets (32%). The use of inputs in livestock production was low with the exception of de-wormers (73%), minerals (70%), water (58%) and acaricides (68%). The major constraints to various inputs for livestock production was distance to the input market (35%) and high price of the inputs and unavailability of inputs (19% each).

The yields for maize, common bean and Irish potato were 848, 259 and 1190 Kg/acre in Season 1 respectively. The same crops had yields of 794, 277 and 847 Kg/acre respectively for season 2. Livestock breeds included had the local cattle, cross breed and camel with a productivity of 3.6, 4.1 and 12 litres/animal/day respectively during the dry season and 4.6, 4.6 and 17.5 litres/animal/day respectively during the wet season.

Mechanization was mostly limited to pumping water (43%) from boreholes and ploughing (33%).The main value addition practices for cereals were milling (84%), de-hulling (9%) and grading (8%). Besides grading (60%) and de-hulling (40%) were value addition practices in pulses. Value addition in milk was fermenting (46%), boiling (48%), making yoghurt (3%) and cooling (2%).

Most of the crops grown were used for both domestic consumption and as a source of income for the households. In the year under study, 1840 Kg of maize was produced but 11,184 Kg was sold. Beans had 758 Kg produced but 590 Kg sold while Irish potato had 786 Kg produced while 3,509 Kg was sold. Only 24% of the households selling milk and 23% of those selling live animals had contractual arrangements. The eggs produced were normally sold to individuals.

Access to financial services was minimal while none of the households reportedly accessed insurance services. Similarly, market information service was accessed by less than 30% of respondents. More households accessed agricultural services from both private and public providers and had over 65% satisfaction with the services. Access to the private agricultural service providers was higher than to the public service providers.

Using the level of total income as a proxy indicator of vulnerability, the sample households were categorized into vulnerable and non-vulnerable. This approach gave 66% vulnerable and 34% non-vulnerable households. The non-vulnerable households possessed or accessed more productive resources that made them better off in adopting or coping with shocks. Data from the Commission of Revenue Authority on counties placed the poverty rate of Laikipia at 51%.

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Analysis of food and nutrition security indicated that overall 80% of the households did not have enough food to meet their dietary needs. In particular, about 89% of male-, 94% female- and 78% youth-headed households were food and nutrition insecure. In terms of dietary diversity-a proxy for quality of diet, out of a maximum score of 12, MHHs scored 1.8, FHHs 1.68 and YHHs 1.75 implying all households were not having a good quality diet. In addition to majority of the households not having enough food, having low quality diets, 51% of the households consumed less than three food groups within one week.

The main National Resource Management (NRM) practices were minimum tillage, crop rotation, intercropping, mulching, cover-cropping, terracing and planting pits which are key to conserving the natural resource base. The county had a high level of awareness on climate change and effects such as reduction in water volumes, drying up of wells and soil degradation.

The gender disparity was quite evident in the county. For instance, family labour was provided by females (49%) and youth (37%). Hired labour for livestock was mainly done by the youth. From the 161 households, 18.6% of the adult males used inputs while only 4.3% of the adult females used inputs. Decision making on food crops was mainly done by the females while the males made decisions in cash and perennial crops. Men used more machinery than the women. On farm income for the male, female and youth headed households was KES 499,779, 175,974 and 184,875 respectively. Off-farm income for the male, female and youth headed households was KES 20,974, 3,772 and 27,460 respectively. Income per capita for the male, female and YHHs was highest for the male- and least for the FHHs.

The study depicted a low level of tertiary education, limited income sources (mainly on-farm), gender disparity in input use, income and decision-making. The major constraints to input use in crop and livestock production were high prices and a long distance to the market. Productivity of annual and perennial crops in both Season 1 and 2 was low which could be attributed to low usage of production inputs, low access of agricultural services and low mechanization of farm activities. There was low use of input and rudimentary value addition methods. A high proportion of households did not have enough food to eat. The awareness level for NRM/climate change issues was high but use of NRM/climate change technologies/strategies was not adequate.

To increase and sustain agricultural production and the extension arm of the county government in partnership with the private sector should develop programmes that ensure availability of farm inputs and farmers are sensitized on their use, enhance service provision to farmers, promote value addition by investing in infrastructure, undertake capacity building and provision of relevant technologies and promote NRM to conserve the resource base and also adapt to and mitigate against climate change. Gender parity through development of programmes that address the needs of the various gender categories should be undertaken. Finally diversification of food production including high value and nutritious foods both for home consumption and sale.

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Volume I Household Baseline Survey Report - Laikipia County 1

1.0 INTRODUCTION

1.1 County profile

1.1.1 Location and sizeLaikipia County is one of the 47 counties in the Republic of Kenya. It covers an area of 9,462 km2 and is ranked as the 15th largest county in the country by land size (Laikipia CDP, 2013). It borders Samburu County to the north, Isiolo County to the north east, Meru County to the east, Nyeri County to the south east, Nyandarua County and Nakuru County to the south west and Baringo County to the west.

1.1.2 Administrative and Political UnitsThe county is divided into five administrative sub-counties (formerly districts) namely: Laikipia Central, Laikipia East, Laikipia North, Laikipia West and Nyahururu sub-counties. The sub-county headquarters are at Lamuria, Nanyuki, Dol Dol, Rumuruti and Nyahururu respectively. The county is further sub-divided into 3 constituencies, 15 divisions, 51 locations and 96 sub-locations respectively as indicated in Table 1.1.

Table 1.1: Administrative and political units, Laikipia County

Constituencies Sub-counties Area km2 No. of divisions No. of Locations No. of Sub-locations

Laikipia East Laikipia Central 1,107.3 4 7 11Laikipia East 1,863.1 2 7 16

Laikipia North Laikipia North 2,600.2 1 9 14Laikipia West Laikipia West 3,088.1 4 14 28

Nyahururu 803.3 4 14 27Total 9,462 15 51 96

Source: Laikipia County Development Profile (2013)

1.1.3 Demographic characteristics

According to the 2009 Kenya Population and Housing Census (KPHC) report (GOK, 2010), the total population for the county stood at 399,227 people of which 198,625 were males and 200,602 were females. This population was projected to be 427,173 persons in 2012 and is also expected to rise to 457,514 and 479,072 in 2015 and 2017, respectively. The county has a large youthful population with over half of the county’s population being below the age of 35 years. This trend is expected to continue up to the year 2017.

The total number in the employment category was 214,981 persons (comprising of 105,734 males and 109,247 females) representing 53.8% of the county population. The county labour force recorded 41,450 households being active economically in 2009. The number of the employment category is 230,030 in 2012 and is projected to increase to 246,368 and 257,977 in 2015 and 2017 respectively. This calls for programmes that will create employment and other income generating opportunities for this ever increasing population to reduce levels of unemployment and its associated adverse effects in the county.

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1.1.4 Land availability and useOf the total land mass, arable land constitutes 1,984 km2, non-arable land constitutes approximately 7,456km2, and urban areas constitute 243.3km2. There are 5 distinct land use patterns heavily influenced by the climatic conditions and the ecological zones. These include: pastoralism, mixed farming, ranching, agro-pastoralism, and marginal mixed farming.

The average farm size for small scale holders is 0.8ha while for large scale holder’s is 8.1ha. The ranches in the county hold an average of 4,048.58ha. Average land holding in the group ranches per household is 3.33ha. The percentage of land owners with title deeds is 65.3. There are about 4,712 squatters in the county distributed in Kwa Mbuzi (1,021); Kahurura (1,090); Kandutura (400); and Ontilili villages.

The county has 580km2 of gazetted forest land. Mukogodo is one of natural forests within the county. Artificial forests include Lariak, Marmanet, Ng’arua, Rumuruti and Shamaneik. Part of the forests especially in Ng’arua and Rumuruti have been excised for agricultural and settlement purposes.

The South-western part of the county has the highest potential for forestry and mixed farming due to its favourable climatic conditions. These conditions have resulted in some areas especially around Marmanet being the most densely populated. The eastern and northern parts of the county are suitable for grazing while the plateau lying in the central and the northern parts of the county is suitable for ranching.

Laikipia County is richly endowed with wildlife, which is widely distributed in the semi-arid areas extending to Samburu, Meru and Mt. Kenya wildlife corridors/ecosystems. Most of the wildlife is found in the large scale private ranches, which occupy over 50% of the total area of the county. The major tourist attractions are wildlife, the unique Maasai cultural practices and the Thomson Falls. The proximity to Mt. Kenya, Meru, Aberdares and Samburu game parks have greatly boosted tourism within the county through provision of hospitality services to the tourists. The importance of wildlife is manifested by existence of a strong ranching organization called the Laikipia Wildlife Forum.

There are two main industries, namely the Kenya Cooperative Creameries (KCC) plants, located within the Nanyuki and Nyahururu towns and emerging flour milling plants at Nanyuki and Ng’arua. There are many other activities mainly in the informal sector dealing with furniture workshops, welding, garages, metal works, potteries, phones, radio and TV repairs among others.

1.2 Role of agriculture in the countyThe high and medium potential land constitutes about 21% of the total county’s land area while the remaining 79% is low potential hence unsuitable for crop farming. The major soils are mainly loam, sand and clay. Black cotton soil which has inherent fertility spreads in most parts of the plateaus. The dark reddish brown to red friable soils and rocky soils are mainly found on the hillsides. The annual rainfall varies between 400mm and 750mm though higher annual rainfall totals are observed on the areas bordering the slopes of Mt. Kenya and the Aberdare Ranges. North Marmanet receives over 900mm of rainfall annually, while the drier parts of Mukogodo and Rumuruti receive slightly over 400mm annually. The long rains occur from March to May while the short rains are in October and November. The annual mean temperature of the county ranges between 16o C and 26o C. Laikipia is drained by the Ewaso Nyiro River and its tributaries which originates from Mt. Kenya and the Aberdares. Recovery of farmland has been successful through farm forestry.


Agriculture is recognized in Laikipia County; over 60% of the county population derives their livelihoods from this sector and it contributes 75% of the household incomes. The limiting factors to agricultural production are poor weather conditions characterized by frequent dry spells and poor rainfall distribution. In 2012, crop farming and livestock keeping sub-sector employed 141,383 persons comprising 47% of the employed population. The main crops grown are maize, bean, wheat, Irish potato, cabbage and tomato. The main types of livestock raised are cattle, sheep, goat, camel, donkey, poultry and pig. Livestock production is an important source of livelihood in the county and over half of the land area is under livestock rearing activities. Population of cattle, goat and sheep has been declining mainly due to drought, diseases, poor management of pastures, poor extension services, inbreeding, high cost of veterinary drugs and insecurity discouraging farmers from investing in the industry. However, rearing of emerging livestock namely rabbits and dairy goat has increased significantly with a great potential for ostrich farming. The camel continues to be of great use as it can cope with drought conditions.

There is a growing fish farming population in the recent past particularly promoted under the Economic Stimulus Programme (ESP). The county has about 360,000m2 of fish ponds with cat fish, common cap and Tilapia species.

The county received about KES 2.5 billion from the major crops, with maize and beans contributing about KES 1.7 billion and KES 0.4 billion, respectively accounting for 82% of the estimated income from crops (Table 1.2).

Table 1.2: Crop production in the County

Crop Production (Tons) Value (KES-million)Dry maize 65,944 1,715.27Beans 7,299 380.62Tomato 7,654 174.96Wheat 9,241 277.86Total 2,548.71

Source: Economic Review of Agriculture, 2012

The county received KES 705 million from major livestock products with milk and beef generating KES 83 million and KES 294 million, respectively (Table 1.3).

Table 1.3: Quantity and value of livestock and livestock products

Product Quantity (tons) Value (KES million)Beef (kg) 840,000 294.0Mutton (kg) 900,000 270.0Milk (litre) 3,350,000 83.8Poultry meat (kg) 60,000 24.0Egg (Trays) 54,000 14.6Honey (kg) 36,000 10.8Pork (kg) 18,000 5.4Fish (kg) 25,401 2.7Total 705.26

Source: Laikipia County Development Profile, 2013


Some of the challenges to agricultural production in the county are:

• Large proportion of the land is low potential to agricultural production

• Livestock diseases, poor management of pastures, inbreeding, high cost of veterinary drugs and insecurity

• Wildlife/livestock/human conflict

• Persistent and prolonged droughts

• Incidences of cattle rustling

• Poor land use, land degradation and inappropriate land tenure system

• Inadequate water supply and poor quality


2.0 RATIONALE OF THE BASELINE SURVEYThe Agricultural Sector Development Support Programme (ASDSP) is intended as a sector Programme, aligned with the Government of Kenya (GOK) commitments to the agricultural sector through the Agricultural Sector Development Strategy 2010–2020 (ASDS) and the Comprehensive African Agricultural Development Programme (CAADP) Kenya Compact. The ASDSP furthermore contributes to the realization of Kenya’s wider development goals as expressed in the Millennium Development Goals (MDGs), Vision 2030 and Kenya’s Constitution (2010).

The programme’s overall goal is to transform Kenya’s agricultural sector into an innovative, commercially orientated and modern industry that will contribute to poverty reduction, improved food security and equity. The main purpose of ASDSP is to increase equitable income, employment and improved food security of male and female target groups as a result of improved production and productivity in the rural smallholder farm and off-farm sectors’

The ASDSP supports Programme coordination within its primary outcome areas (environmentally resilient and socially inclusive value chain development and associated sector coordination) at national and county levels. The ASDSP is open to contributions from interested development partners.

ASDSP has defined its results indicators in the program log-frame but the baseline levels for these indicators are currently unavailable. A baseline survey was therefore necessary in order to establish the current status (or level) for each indicator. The baseline information for the indicators will be the guiding pillars to measure the program’s achievements and outputs. The information will also help in developing an appropriate tool for the Monitoring and Evaluation (M&E) of program interventions that target specific results (outputs, outcomes and impacts). Furthermore, the baseline survey will generate and develop an information base comprising the detailed relevant information of the general and targeted beneficiaries in the program’s working areas.

The specific objectives of the baseline study were: • To collect data on and analyse the verifiable indicators from the program log-frame • To collect and analyze the relevant information of existing situation of program’s target

beneficiaries including the poor and vulnerable groups, service providers, and/or related stakeholders

• Enhance understanding on the characteristics and determinants of actors’ activities, management practices, factors influencing their service delivery and inclusion of key stakeholders in their planning and programming.


3.0 METHODOLOGY3.1 Approach The survey was carried out by the Ministry of Agriculture Livestock and Fisheries (MoALF) through the ASDSP in collaboration with Kenya Agricultural Research Institute (KARI) and the University of Nairobi (UoN). Both primary and secondary data collection methods were employed to gather data needed for the baseline survey. Secondary data sources comprising relevant program and sub-sector documents, county level data bases and related literature were used. Primary data were collected from sampled beneficiaries in the county. The survey, which was conducted during September-October 2013, was structured and managed in a way that ensured high data quality. Specifically, the survey focused on collection of the following data:• Household socio-economics characteristics• Farm characteristics• Level of production and productivity for major agricultural commodities• Adoption and access to improved agricultural technologies• Costs of major inputs used and prices received from selling major commodities• Index of labor availability and wage rates paid for farm and off-farm work• Access and use of machinery and various equipment in farm operations• Pre-harvest and post-harvest food losses and main causes• Quantities of major commodities consumed and marketed• Level of primary target groups’ engagement in agro-enterprises• Extent of primary target groups’ access to productive assets disaggregated by gender and

vulnerability • Extent of primary target groups’ involvement in local farmer organizations• Extent of access to social protection services• Extent of involvement by different gender categories in decision making at the local level • Average and disaggregated (male and female-headed) household on-farm income• Average and disaggregated (male and female-headed) household off-farm income• Level of food and nutrition security in both male and female-headed households• Household asset ownership index by gender and vulnerable groups• Level of farmers’ membership in local farmer organizations, by gender and vulnerability• Extent to which primary target groups’ (by gender and vulnerability) are addressing their

production and marketing needs through horizontal organizations • Extent to which primary target groups’ access financial (credit) and insurance services and

prevalence of savings • Prevailing climate-related risks, the extent of primary target groups’ awareness of climate-related

risks, level of response to climate-related risks

The approach used for collecting these data involved the following steps:• Defining the sample, identification of the respondents and gauging their accessibility• Reflecting on the research design and collection of secondary data• Preparation of research instruments and recruitment/training of survey supervisors who were

to be responsible for data collection and entry• Obtaining permission for data collection from the relevant authorities. This was done through

the County Committees • Pre-testing and revisions of instruments • Sampling, geo-referencing sampled households, recruitment/training of Geographical

Information System (GIS) mappers and identification of sampled households• Recruitment/training of enumerators and collection of primary data • Data entry and analysis • Report preparation• Validation of information in the report at county level


3.2 Sampling procedure The Laikipia County baseline survey was part of a larger national survey covering all the 47 counties with an overall sample of 12,651 agricultural households. Using a Proportionate to Population Size (PPS) sampling method, a sample of 161 households was selected for the county out of its 1,682 agricultural households. The survey was confined to the prominent production systems (agro-ecological zones) and the county sample size was randomly distributed to those areas based on the population density of each production system determined using the Proportionate to Population Size technique, based on total number of farming households in each county. Figure 3.1 shows the areas of Laikipia County selected for the household baseline survey and the locations of the randomly sampled households which were geo-referenced.

Source: Kenya Bureau of Statistics 2010 Figure 3.1: Map of Laikipia County showing the sampled households locations

3.3 Data Collection and Analysis Prior to data collection local county supervisors were sensitized on the objectives, scope and expected logistics for the planned survey. Enumerators and data entry clerks were recruited and trained on the survey instrument (questionnaire).

All households were geo-referenced by the GIS mappers who had earlier been recruited and trained. The mapping of households was done prior to the actual data collection. Thereafter, data were collected by enumerators using a structured questionnaire during late September to early October 2013. Data entry was done at KARI Embu using Microsoft Excel software and later exported to the Statistical Package for Social Sciences (SPSS) which was used for data analysis. During data analysis, results were summarized using descriptive statistics (frequencies, means, measures of dispersion) and other relevant statistics. In the report, area is expressed in acres, where 1 acre = 0.4047 hectares.


4.0 SURVEY RESULTS This chapter presents the results of the various analyses done from the sample survey data. The results presented focus mainly on the current status or levels of the various indicators in the ASDSP logical framework.

4.1 Household socio-economics and farm characteristics

4.1.1. Household size and genderMost of the households were male headed and managed (66.4%) followed by youth headed (22.4%) and female managed (11.2%). A typical household in the county had an average of four members. Disaggregated by gender, household mean size of MHH was five. Female headed and YHHs had mean size of three and four members respectively. The sex of household members was of about equal proportions with 49.7% males and 50.3% females. The mean age of household head was 48 years. Disaggregated by gender, mean age of the household head were 53 years, 59 years and 29 years in male headed, female headed and YHHs respectively.

4.1.2 Level of Education About 68% of all the household heads had attained either upper primary or secondary school of education. Those who had attained certificate/vocational level of education and above were only 2%. Female adult-headed households constituted 11% of all the households interviewed, 41% of whom had no formal education, while 53% had attained upper primary and secondary level of education (table 4.1).

Table 4.1: Distribution of levels of education attained by household head

Level of education Proportion (%) of household head (n=157)MHH FHH YHH Overall

No Education 12.7 4.5 5.1 22.3

Lower Primary 5.7 0.6 1.3 7.6

Upper Primary 29.9 4.5 11.5 45.9

Secondary 16.6 1.3 4.5 22.3

Certificate/Vocational/ Craft 0.0 0.0 0.6 0.6

Diploma 1.3 0.0 0.0 1.3

Tertiary 0.0 0.0 0.0 0.0

Total 66.2 10.9 23.0 100

4.1.3 Primary occupation of householdsCrop and/or livestock farming were the main primary occupation in the county (Table 4.2). Livestock and livestock products trading was a preserve for the adult males while farm labour on other farms was provided by the youth. Adult males were more likely to be in formal employment and also more likely to earn from pension compared to adult females and youth. Eighty 82.9% of households were employed on-farm (crop and livestock farming, farm labourer on other farms and trading in livestock and livestock products) while 17.1% were employed off-farm.


Table 4.2: Primary occupation of household heads

Type of occupation Proportion (%) of household heads engaging in occupation

Overall(n=161)

MHH (N=107) FHH(N=18) YHH (N=36)

Crop and/or livestock farming 81.6 88.8 73.6 81.3

Formal salaried employment 4.9 0.0 8.8 4.6

Self-employed business (trade/services) 5.8 5.6 14.7 8.7

Farm labourer on other farm 0.0 0.0 2.9 1.0

Old/Retired/Pensioner 1.9 0.0 0.0 0.6

Livestock and livestock product trading 1.9 0.0 0.0 0.6

Other occupation 3.9 5.6 0.0 3.2

4.1.4 Household members with chronic illness, incapacitated or under social protectionOut of the sampled households, 6.2% reported having members with chronic illness, incapacitated or under social protection. Disaggregated by gender, 7.5% of MHHs and 5.6% of the YHHs reported having members with the same condition in their households.

4.1.5 Land ownership and accessAverage farm size was 3.7 acres, however when disaggregated by gender, MHHs owned 3.5 acres while FHHs owned 3.4 acres and youth headed owned and managed 4.7 acres.

On average most households owned and/or accessed two parcels of land. The adult females accessed one parcel while adult males and youth accessed two parcels each.

The average distance for parcel one was 2 Km from the homestead location. The other parcels of land were eight to 11 Km away from the parcel where the homestead was located.

In all gender categories for Land parcel 1, the household head and/or spouse had the most ownership (72%) followed by joint household member and relative (12%), and rented in (9%) (Table 4.3).

In MHH and FHH, the Land parcel 2 was also mostly owned by household head (47%), followed by rented in (28%) and joint ownership with a relative (12%). In YHHs and adult MHHs, the third parcel was mostly rented in while in FHH it was mostly jointly owned by household head and spouse.


Table 4.3: Ownership of different parcels of land accessed by the households

Parcel of land No.Household head and/or

spouse

Rented in

Communal Relative non-household

member

Joint household member and

relative

Land parcel 1 (n=143) 71.6 8.9 1.3 6.4 11.7MHH (n=93 79.6 3.2 1.1 7.5 8.6FHH (n=16) 100.0 0.0 0.0 0.0 0.0YHH (n=34) 35.3 23.5 2.9 11.8 26.5Land parcel 2 (n=50) 47.3 28.4 4.8 7.1 12.2MHH (n=39) 58.6 24.1 3.4 10.3 3.4FHH (n=3 ) 66.7 33.3 0.0 0.0 0.0YHH (n=18) 16.7 27.8 11.1 11.1 33.3Land parcel 3 (n=18) 53.5 31.8 0.0 3.0 11.6MHH (n=11) 27.3 45.5 0.0 9.1 18.2FHH (n=1) 100.0 0.0 0.0 0.0 0.0YHH (n=6) 33.3 50.0 0.0 0.0 16.7

* Land parcel 1 refers to the land where homestead is located, usually within 2kms of the homestead* Land parcels 2 and 3 refer to the land 8-13kms away from the homestead

The access to different parcels of land owned by the household members is presented in Table 4.4. The access/use of land parcel where the homestead was located (Land parcel 1) was 44% by joint household head and spouse and 29% by head of the household. Similar trend was reported for the other two parcels of land. The access by spouse of household head alone was only 5% for the parcel of land where the homestead was located. Female relative had less than 1% access to the parcel where homestead was located.

Table 4.4: Access to different parcels of land by household members

Parcel of land No.

Percent

Head of household

(HH)

Spouse of HH

HH and spouse

Household member and

relative

Male relative

Female relative

Non-relative (male)

Land parcel 1 29.0 4.6 43.5 17.6 1.5 0.8 3.1Land parcel 2 33.3 4.8 45.2 11.9 4.8 0.0 0.0Land parcel 3 38.5 0.0 46.2 7.7 7.7 0.0 0.0

4.1.6. Land Tenure Eighty one percent of the MHHs owned land where 63.5% had titles and 17.5% owned but did not have titles. Disaggregated by gender, 91% of MHHs own land, 95% of FHH and 65.5% of YHHs owned land either with title deeds, allotment letter or without any formal title.

Sixty percent of households surveyed had title deeds, 18% owned land but did not have titles, 14% leased land and 7% used communal land (Table 4.5). Sixty five percent of the FHH, 63.5% of the MHHs and 50.0% of the YHHs had land with formal title.


Table 4.5: Proportion of land tenure system by gender

Type of tenureGender of household head

Overall(n=161)Adult male

(n=107)Adult female

(n=18) Youth (n=36)

With title deed/allotment letter 63.5 65.0 50.0 60.0Owned but no formal title 17.5 30.0 15.5 18.1Leased/Rented in 13.9 5.0 17.2 14.0Communal 5.1 0.0 15.5 7.4Squatters 0.0 0.0 1.7 0.5

Ownership of different parcels of land as described in section 4.1.5 above, where Land parcel 1 was where the homestead was located and parcels 2 and 3 were within 8-13kms from the homestead is shown in Table 4.6. Ownership of Land parcel 1 with title or allotment letter was 65% while ownership without formal documents was 27%. Disaggregated by gender, ownership of Land parcel 1 with formal title or allotment letters in MHHs was 68%, 59% in FHH and 57% in YHHs. This was followed by ownership of land with no formal document at 22%, 35% and 17% in male adult, female adult and YHHs respectively (Table 4.6). This indicates that most households (over 60%) had incentives to borrow and undertake major investments because they have land collateral.

Table 4.6: Land tenure system for different parcels of land owned by the household

Proportion (%) of household under systemFormal title

or allotment

letter

Owns but has no formal

document

Lease/Rented in

Has communal rights to use

land

Has land consid-ered as own but

not allocated

Land parcel 1(n= 150) 64.7 22.7 6.7 6.0 0.0Male headed (n=98) 68.4 22.4 5.1 4.1 0.0Female headed (n=17) 58.8 35.3 5.9 0.0 0.0Youth headed (n=35) 57.1 17.1 11.4 14.3 0.0Land parcel 2 (n=48) 52.1 8.3 27.1 12.5 0.0Male headed (n=28) 50.0 7.1 35.7 7.1 0.0Female headed (n=2) 100.0 0.0 0.0 0.0 0.0Youth headed (n=18) 0.0 11.1 16.7 22.2 0.0Land parcel 3 (n=48) 41.2 5.9 41.2 5.9 5.9Male headed (n=28) 54.5 0.0 36.4 9.1 0.0Female headed (n=2) 100.0 0.0 0.0 0.0 0.0Youth headed (n=18) 0.0 20.0 60.0 0.0 20.0

4.1.7 Allocation of land for different uses

Most people allocated land to homestead (39%), natural pastures (28%) and subsistence crop production (18%) (Table 4.7). On average, commercial crop production and improved forages production constituted 8.4% of all land owned while natural pastures constituted 28%. This shows that livestock rearing is an important activity in the county.


Table 4.7: Land allocated to different uses by different household types

Land use typeProportion (%) of land allocation by Gender

Male Female Youth OverallHomestead 46.3 11.7 18.4 39.1

Subsistence crop production (for household consumption) 17.3 29.3 17.2 18.6

Commercial crop production (for marketing) 5.2 2.5 5.6 5.0

Improved pastures/forages production 3.6 1.8 3.4 3.4

Natural pastures 22.8 40.0 49.1 27.9

Woodlot 3.4 10.7 1.5 3.9

Fisheries 0.0 0.0 0.0 0.0

Unusable land (swampy, rocky, hilly) 1.4 4.1 4.7 2.1

4.2 Production, use of inputs and decision-making in crops, livestock and fisheries

4.2.1 Use of Labour in crop and livestock production The labour contribution in crop and livestock production, disaggregated by gender, is shown in Figure 4.1. Family labour in crops production was mainly provided by adult females (49%) and the youth (37%) while hired labour was provided mainly by adult females (58%) and adult males (28%). Family labour in livestock production was provided by all family members (youth, adult males, and adult females in that order), hired labour in livestock production was provided predominantly by the youth (Figure 4.1).

Figure 4.1: Contribution (%) of labour in crop and livestock production, by gender


4.2.2. Use of agricultural inputs, rates, costs and challenges 4.2.2.1UseofvariousinputsindifferentannualcropsduringSeason1(August2012-February2013) The adoption levels of crop varieties by types of households are presented in Figure 4.2. Use of improved varieties of maize and tomato was the highest at 65% and over 80% respectively while it was lowest in beans and Irish potato at 5% and 4% respectively. Between 50 and 58% of respondents used local varieties of beans and Irish potato.

Figure 4.2: Households using different seed types during Season 1

Households’ use of various inputs for crop production are shown in Table 4.8. Seed/planting material had the highest adoption rate of 73%. Basal and top dress fertilizers had adoption rates of 16%. Proportionately, more males than females adopted the various management practices.

Table 4.8: Household use, disaggregated by gender, of various inputs in annual crops during Season 1

Input used

Level of input use by Gender of household head (HH)

Overall(n=161)

Adult male Adult female Youth

% within Male HH(n=107)

% of total HH

% within Female HH

(n=18)

% of total HH

% within Youth HH

(n=36)

% of total HH

Improved Seed/planting material 73.8 49.1 83.3 9.3 14.9 14.9 73.3

Herbicides 5.6 3.7 16.7 1.9 8.3 1.9 7.5Basal fertilizer 15.9 10.6 22.2 2.5 13.8 3.1 16.1Top dress fertilizer 17.8 11.8 22.2 2.5 8.3 1.9 16.1Organic manure 28.0 18.6 38.9 4.3 27.8 6.2 6.2Foliar feed 14.0 9.3 5.6 1.2 22.2 5.0 15.5Irrigation water 11.2 7.5 33.3 3.7 13.9 3.1 14.3Field pesticides 11.2 7.5 22.2 2.5 13.9 3.1 13.0Storage pesticides 7.4 4.9 22.2 2.5 11.1 2.5 10.0


4.2.2.2 Decision-makinginproductionofannualcropsduringSeason1 Decision making depends on the type of crop. Males dominated in making decisions for market oriented crops such as maize and tomato (Table 4.9). Adult females dominated in food crops such as Irish potato and beans. They also dominated in making decisions in bulb onion. Male youths dominated decision making in growing market oriented crops such as wheat, cabbage and snow pea.

Table 4.9: Decision making in production of annual crops, by gender

Crops grown Sample (n)Level of decision-making (%) by gender

Adult male Adult female Youth male Youth femaleMaize, dry 102 44.1 34.3 13.7 7.8Beans (common) 91 34.1 45.1 11.0 9.9Irish potato 45 33.3 44.4 13.3 8.9Cabbage 12 41.7 8.3 50.0 0.0Tomato 22 54.5 9.1 36.4 0.0Sweet potato 7 42.9 14.3 42.9 0.0Bulb onion 7 28.6 42.9 28.6 0.0Snow pea 5 40.0 0.0 60.0 0.0Fodder maize 6 33.3 16.7 50.0 0.0Wheat 3 33.3 0.0 66.7 0.0

4.2.2.3UseofinputsindifferentannualcropsduringSeason2(March-August/September2013) The adoption levels of crop varieties by households are presented in Figure 4.3. There were differences in the type of seed used for the priority crops grown in season 2. About 70% of respondents who grew maize used improved varieties. Respondents growing fodder maize used improved varieties while wheat was grown using recycled improved varieties.

Figure 4.3: Households use of different seed types during Season 2


More males than females had more use of organic manure, basal and top dress fertilizer, foliar feed and field pesticides in Season 2 (Table 4.10). All management practices had relatively low adoption (less than 20% except use of improved seeds and other planting materials (28%)). Adoption was higher among males than females for most of the management practices. The same pattern was observed during the second rain season.

Table 4.10: Household use of various inputs in annual crops during Season 2

Input used

Level of input use by gender of household head

Overall (n=161)Male Female Youth

% within Male Adult HH (n=107)

% of total HH

% within Female Adult

HH (n=18)

% of total HH

% within Youth HH

(n=36)

% of total HH

Improved seed/planting material 27.1 18.0 27.8 3.1 30.6 6.8 28.0

Herbicides 4.7 3.1 5.6 0.6 8.3 1.9 5.6

Basal fertilizer 12.1 8.1 11.1 1.2 8.3 1.9 11.2

Top dress fertilizer 10.3 6.8 5.6 0.6 2.8 0.6 8.1

Organic manure 16.8 11.2 11.1 1.2 8.3 1.9 14.3

Foliar feed 12.1 8.1 5.6 0.6 11.1 2.5 11.2

Irrigation water 9.3 6.2 11.1 1.2 5.6 1.2 8.7

Field pesticides 11.2 7.5 5.6 0.6 5.6 1.2 9.3

Storage pesticides 1.9 1.2 5.6 0.6 0.0 0.0 1.9


The main annual crops included dry maize, common beans and Irish potato. Main inputs were seed, organic manure and planting fertilizer (Table 4.11).

Table 4.11: Level of input use at farm level for annual crops by gender

Input Crop Season Quantity used (Kg) by HH

Adult male Adult fe-male Youth Overall

Planting material Dry Maize Season 1 24.2 71.0 72.5 41.4Season 2 7.4 5.0 10.7 7.8

Common (beans)

Season 1 22.2 30.6 65.6 32.7Season 2 16.7 82.7 12.9 23.3

Irish potato Season 1 44.2 61.2 68.1 52.3Season 2 101.9 185.0 25.3 103.3

Planting fertilizer Dry Maize Season 1 28.5 20.0 3162.5 922.7Season 2 900.7 - 100.5 580.6

Common (beans)

Season 1 19.0 - - 19.0Season 2 5.0 - - 5.0

Irish potato Season 1 1,03.4 - 25.0 75.8Season 2 10.0 - 6250.0 3130.0

Topdressing fertilizer

Dry Maize Season 1 27.5 8100.0 - 2718.3Season 2 112.5 - 150.0 125.0

Common beans

Season 1 - - - -Season 2 - - 200.0 200.0

Irish potato Season 1 - - - -Season 2 - - - -

Herbicides Dry maize Season 1 1,667.3 - 1.0 1250.8Season 2 5.0 - 1.0 3.0

Common (beans)

Season 1 - 0.3 2.0 1.1Season 2 - - 0.5 0.5

Irish potato Season 1 - 0.1 1.0 0.6Season 2 - - - -

Organic manure Dry Maize Season 1 1,196.4 14102.6 1381.8 3313.3Season 2 3,232.5 4000.0 4125.0 3464.5

Common (beans)

Season 1 3,647.1 1800.0 311.3 2704.8Season 2 2,563.3 - 100.0 1947.5

Irish potato Season 1 1,575.0 1800.0 - 1650.0Season 2 7,000.0 - - 7000.0

- = input not used

Use of various inputs was minimal in perennial crops (Table 4.12). None of the households reportedly used top dressing fertilizer on any perennial crop, indicating minimal use of inputs. The only inputs used in perennial crop production were planting material and limited application of manure. Only pasture and lucerne crops received both planting material and organic manure. None of the households used herbicides in growing perennial crops. This indicates that perennial crops are not managed optimally for production.


Table 4.12: Level input use for perennial crops at farm level, by gender

Input/Crop Quantity of fertilizer (kg) used by household head

Adult male Adult female Youth OverallSeeds/planting materialPomegranate 2.5 1.0 - 1.3Banana 1.0 - - 1.0Tree, multipurpose 50.4 105.1 20.0 67.5Pasture 14.0 - - 14.0Lucerne 4.0 - - 4.0Organic ManurePasture 450.0 - - 450.0Lucerne 3,000.0 - - 3,000.0- = input not used

Levelofdecision-making inproductionofannualcropsduringSeason2Males dominated in decision making for most of the crops with adult females making most decisions on wheat production. Adult males made decisions on particular market oriented crops such as tomato and cabbage (Table 4.13). The youth did not have a particular domain in decision making in crops grown.

Table 4.13: Decision-making in annual crop production during Season 2

Crops grown Sample (n) Proportion (%) of decision-making by household headAdult male Adult female Youth male Youth female

Maize, dry 86 38.4 32.6 20.9 8.1Beans (common) 72 41.7 36.1 16.7 5.6Irish potato 54 38.9 37.0 11.1 13.0Tomato 15 73.3 0.0 26.7 0.0Wheat 8 0.0 75.0 25.0 0.0Cabbage 10 40.0 0.0 30.0 30.0

4.2.2.5Useofinputsindifferentperennialcrops Apart from planting material, farmers who grew perennial crops used organic manure (Table 4.14) but only at 5% of the households. None of the respondents used basal and top dress fertilizers, herbicides, foliar feed, irrigation water or field and storage pesticides. This observation indicates minimal use of inputs in perennial crops in the county. Proportionally, more FHH used planting material and organic manure than male headed and YHHs.

Table 4.14: Households using various inputs in perennial crop production

Input used

Gender of household head

Adult male Adult female Youth Overall(n=161)

% within (n=107) % of total % within

(n=18)% of total

% within (n=36) % of total

Planting material 21.5 14.3 33.3 3.7 13.9 3.1 21.1Organic manure 5.6 3.7 11.1 1.2 0.0 0.0 5.0


4.2.2.6Maindecision-maker inproductionofperennialcrops Multi-purpose trees and pasture were the main perennial crops in most households. Most of the decisions in production of perennial crops were made by adult males (Table 4.15). Female youth were involved in production of pasture but not in production of multi-purpose trees.

Table 4.15: Decision-making in perennial crop production by gender

Crop grown Sample (n)Percent (%)

Adult male Adult female Youth male Youth femaleMulti-purpose trees 56 62.5 14.3 23.2 -Pasture 72 58.3 16.7 23.6 1.4

4.2.2.7Constraintsoninputuseincropproduction Table 4.16 shows that most of the households experienced constraints especially in acquiring seed (40%), organic manure (11%), irrigation water (9%), planting fertilizer (8%) and top dressing fertilizer (7%). Proportionately, FHH reported the most number of constraints.

Table 4.16: Households who encountered major constraints in input use

Input used Proportion (%) of the household heads using input Overall(N=161)Adult male Adult female Youth

Seeds 39.2 55.6 36.1 40.4Basal fertilizer 8.4 16.7 2.8 8.1Top dress fertilizer 8.4 11.1 2.8 7.4Organic manure 9.3 16.7 8.3 10.6Herbicide 7.5 11.1 2.8 6.8Irrigation water 10.2 11.1 2.8 8.7Foliar feed 4.7 16.7 2.8 4.9Field pesticides 6.5 11.1 2.8 6.2Storage pesticides 46.7 16.7 0.0 4.9

4.2.2.8Constraintsonusinginputforcropproduction The major constraints encountered across all inputs were high prices (38%) and insufficient income to buy inputs. Distance to input market (32%) and lack of access to inputs at the right time (8%) were the second and third important challenges for input use (Figure 4.4).

Figure 4.4: Main constraints in the use for major crop production


4.2.3 Use of purchased input in livestock production

Incidence of use of most inputs in livestock production (Table 4.17) was low with the exception of de-wormers (73%), minerals (70%), water (58%) and acaricides (68%). Proportionately, more male adult headed households used more livestock inputs than either female adult headed or YHHs.

Table 4.17: Households (by gender) using various inputs in Livestock production

Input used

Use of inputs by gender of household head Overal (N =161)

Adult male Adult female Youth

% within (n=107)

% of total

% within (n=18)

% of total

% within (n=36)

% of total

Artificial insemination (semen) 11.2 7.5 16.7 1.9 16.7 3.7 13.0Concentrates/animal feeds 15.0 10.0 11.1 1.2 0.1 22.2 16.1Acaricides (dipping/spraying) 68.2 45.3 61.1 6.8 72.2 16.1 68.3Mineral supplements (salts) 73.8 49.1 50.0 5.6 69.4 15.5 70.2De-wormers 71.9 47.8 72.2 8.1 75.0 16.8 72.7Vaccines 61.6 41.0 44.4 5.0 61.1 13.7 59.6Fodder/hay/silage/crop residue 31.8 21.1 33.3 3.7 33.3 3.7 32.2Other veterinary drugs 34.6 23.0 33.3 3.7 33.3 7.5 34.2Water 63.6 42.2 61.1 6.8 41.7 9.3 58.4

4.2.4 Decision-making in livestock production, by gender

More males made decisions on all types of cattle, local and dairy goats and sheep than females and sheep (Table 4.18). Decisions on other forms of poultry were made solely by the females.

Table 4.18: Decision-making on livestock production for different livestock types

Livestock species Level (%) of decision-making by household head

Adult Male Adult Female YouthCattle (n=65) 70.8 13.8 15.4Goats (n=51) 80.4 5.9 11.8Sheep (n=59) 64.4 11.9 23.7Chicken (n=83) 31.3 28.9 39.8Other poultry (n=1) 0.0 100.0 0.0

4.2.5 Constraints to using inputs in livestock

The major constraints to various inputs for livestock production was distance to the input market (35% of responses) followed by high price of the inputs and unavailability of inputs (both with 19% of the responses) (Table 4.19). Other constraints were lack of access to inputs in the right package (3%) and lack of access to inputs at the right time (2%).


Table 4.19: Major constraint on using inputs for livestock production

Constraint to livestock productionProportion (%) of households experiencing

constraintAdult male Adult female Youth Overall

High prices/Affordability 9.1 4.0 5.9 19.0

Distance to input market 26.9 1.4 6.1 34.5

Ineffectiveness of inputs e.g. AI services 1.0 0.2 0.2 1.4

Unavailability of inputs 1.8 0.4 5.9 19.0

Lack of access of inputs in right packaging/dosage 0.8 0.2 1.8 2.8

Lack of access to inputs at the right time (lateness) 0.2 1.4 0.6 2.2

4.2.6 Use of machinery in farming activities

Typeofmachinery/equipmentused Forty three percent of households indicated they used some farm machinery/equipment in their agricultural activities. Of these, 39% indicated they used pumps while 31% used tractors. Other machinery and equipment were used by few farmers as shown in Table 4.20. Sixty three percent of those who had used the machinery indicated they hired while 32% indicated they owned them.

Table 4.20: Type and source of machinery/equipment

Type of Machinery Ownership (%) of machine/equipment (n=59)

Overall (%)Owned by HH Owned communally Hired

Tractor 14.7 0.0 85.3 31.4

Plough 0.0 0.0 100.0 1.4

Tractor trailer 14.3 0.0 85.7 2.9

Combine harvester 0.0 0.0 100.0 1.4

Incubator 0.0 0.0 100.0 2.9

Ox/donkey cart 0.0 0.0 100.0 8.6

Pump 60.0 10.0 30.0 38.6

Thresher 12.5 0.0 87.5 7.1

Posho mill 12.5 0.0 87.5 4.3

Generator 0.0 0.0 100.0 1.4

Typesofactivitiesmechanized The main mechanized activities were pumping water (43%) and ploughing (33%). Other mechanized activities are shown in Figure 4.5. For those who hired ploughs, the mean cost of hiring was KES 2,700 to 3,200 per acre. The cost of pumping water per day was KES 300 while harvesting was charged KES 3,000 per acre.


Figure 4.5: Activities mechanized

The main source of machinery or equipment for use on the farm was hired (77%). Only 18% of the households used their own machinery. Some machinery were owned communally (Figure 4.6).

Figure 4.6: Main sources of machinery

4.2.6. Input distribution networks and levels of satisfaction

4.2.6.1.Accesstothenearestagriculturalrelatedservicesandinfrastructure Financial services were the least accessed with only 1% of MHHs reporting having accessed the service while the most accessed service was infrastructure where 94% of respondents reporting having accessed the service (Figure 4.7). Climate information service was accessed by only 3% of the households.


Figure 4.7: Propotion (%) of households accessing different services

Eighty five percent of the surveyed households had access to agricultural-related services. Most respondents accessed the services from private providers (60%) more than from pub-lic providers (40%). Disaggregated by gender, 38% males accessed from public and 62% from private providers while 35% female accessed public and 65% from private. About 47% of youth accessed services from public while 53% from private providers. In all gender catego-ries, most respondents accessed services from private than from public providers (Table 4.21). Table 4.21: Access to agriculture-related services and infrastructure

Agriculture-related service / infrastructure

Degree of access (%) by household head

Institution Type Adult male Adult female Youth Average (%)

Agricultural servicesPublic 31.6 0.0 45.5 34Private 68.4 100.0 54.5 66

InfrastructurePublic 40.8 37.5 53.7 44Private 59.2 62.5 46.3 56

ClimatePublic 20.0 100.0 0.0 29Private 80.0 0.0 100.0 71

FinancePublic - - - -Private 100.0 - - 100

Overall Public 38 35 47 40Private 62 65 53 60

All - = service/infrastructure not accessed

Accesstoagriculturalinformation servicesandinfrastructure

The most accessed services were veterinary, input markets and dipping. Proportionally, more adult females accessed services available compared to adult males and the youth (Table 4.22). However, the youth had more access to output markets compared to other gender.


Table 4.22: Households accessing services, by gender of household head

Proportion (%) of households accessing services

Services Adult male (n=107) Adult female (n=18) Youth (n=36)

Extension 3.7 5.6 0.0

Research 1.9 0.0 5.6

Veterinary 30.0 83.3 44.4

AI 6.5 27.8 11.1

Dipping 13.1 27.8 33.3

Climate 5.6 5.6 2.8

Input market 38.3 83.3 66.7

Output market 11.2 11.1 41.7

Satisfactiontoagricultureservicesandinfrastructure Level of satisfaction in relation to agricultural services and infrastructure measured on a Likert type scale of 1 to 5, showed that satisfaction level was more than 65% in most services offered (Table 4.23). To those who accessed the services, they were 100% satisfied with extension, research, and climate services. More than 65% satisfaction was reported in livestock based services including veterinary and dipping. About 88% and 68% of respondents were satisfied with input and output markets respectively.

Table 4.23: Households satisfied with services

ServicesLevel of satisfaction %

Very dissatisfied Dissatisfied Neutral Satisfied Very satisfied

Extension 0.0 0.0 0.0 40.0 60.0

Research 0.0 0.0 0.0 100.0 0.0

Veterinary 5.4 5.4 8.9 75.0 5.4

AI 6.7 6.7 20.0 60.0 6.7

Dipping 7.7 7.7 0.0 69.2 15.4

Climate 0.0 0.0 0.0 100.0 0.0

Input market 1.5 3.0 7.5 88.1 0.0

Output market 10.5 15.8 5.3 68.4 0.0

Others services accessed included savings, market information, agricultural insurance and agricultural credit. Adult males had most access to these services compared to adult females and youth. Sixteen percent of households accessed savings services. Disaggregated by gender, 9% of MHHs, 2% of adult FHH and 2% of YHHs accessed savings service. Market information was accessed by 29% of the households. Disaggregated by gender, at least 19% of MHHs, 3% of FHH and 7% of YHHs accessed market information services. (Figure 4.8).


Figure 4.8: Access to financial services by household head, by gender

4.2.8 Access to agricultural technologies Only 6% of all the households surveyed accessed at least one new technology within two years of the survey with at least 4.5% for male headed, 17% for female headed and 5% for YHHs. Livestock technologies were the more accessed than crop technologies with adult females reporting more access in both crop and livestock technologies compared to adult males and youth. None of the re-spondents reported to have accessed new technologies in natural resources management (NRM), (Table 4.24). The very low access to all technologies calls for increased extension services in the county. Table 4.24: Households accessing agricultural technologies

Type of technology accessed

% of HH acessing agriculture technologiesAdult male HH (n=107)

Adult female HH (n=18)

Youth HH(n=36%)

Overall(n=161)

Crop technology accessed 2.8 5.6 0.0 2.5

Livestock technologies accessed 2.8 11.1 5.6 4.3Overall agricultural technologies ac-cessed 4.7 16.7 5.6 6.2

4.3 Crop output and productivity 4.3.1 Annual crops 4.3.1.1ProductivityinSeason1foreachcrop(August2012-February2013) Maize, beans and potato were grown by majority of farmers in Season 1 (Table 4.25). These crops were grown on land less than one acre apart from Irish po-tato. Other crops grown were tomato, cabbage and bulb onion.


Table 4.25: Main crop grown in Season 1

CropsArea in

acres (SED)

% of farmers growing priority crop Productivity (kg/acre)

Adult male

(n=107)

Adult female (n=18)

Youth (n=36) Adult male

Adult female Youth

OverallAverage

Maize, dry 0.6(0.1) 84.1 88.9 77.3 759 614 1384 848Beans (common) 0.6(0.2) 81.7 77.8 50.0 251 358 193 259Irish Potato 1.1(0.7) 40.2 44.4 22.7 1390 916 425 1190Cabbage 0.4(0.1) 8.5 16.7 9.1 6601 6533 8000 6860Onion (bulb) 0.3(0.0) 7.3 0.0 4.5 3353 - 2400 3217Tomato 0.4(0.1) 14.6 22.2 27.3 127 65 51 95

4.3.1.2.ProductivityinSeason2(March-August/September2012) Maize, beans and potato were the main crops grown in Season 2 (Table 4.26). Acreage under maize, beans, bulb onion and tomato increased in Season 2 but the reverse is true for Irish potato and cabbage. Maize yields decreased slightly in Season 2 while beans yield slightly increased compared to Season 1. Cabbage yield increased by 50% while tomato yield increased exponentially in season two. Yield of onion bulb decreased by 30%.

Table 4.26: Main crop in Season 2

Crop

Area in acres (SED)

% of HH growing priority crop Productivity (Kg/acre), HHMale headed

(n=153)Female headed (n=35)

Youth (n=20)

Male headed

Female headed

Youth Overall

Maize, dry 1.0(0.1) 61.8 80.0 75.0 820 461 970 794

Common beans 0.7(0.1) 55.9 60.0 66.7 261 317 300 277

Potato 0.7(0.2) 38.2 40.0 50.0 885 110 953 847

Cabbage 0.2(0.0) 2.9 0.0 0.0 160.0 4,000 - 2,380

Onion(bulb) 1.3(0.8) 5.9 0.0 0.0 2,100 - - 2,100

Tomato 1.4(0.5) 20.0 40.0 0.0 1,050 71 - 832

Wheat 0.5(0.1) 5.9 0.0 8.3 870 - - 870 4.3.2 Productivity in perennial crops The predominant perennial crops grown were pastures and multipurpose trees. Pasture was the most important perennial crop and was grown by about 48% of the adult males, 61% adult females and 33% of the youth surveyed (Table 4.27). Multipurpose trees were grown by 38% of adult males, 39% of adult females and 25% of the youth. Pomegranate was grown by only 1% of MHHs. The youth grew pasture, multi-purpose and commercial trees only.


Table 4.27: Main perennial crop

Crop

Mean area in

acres

% of farmers growing priority crop Productivity (Kg / acre)

Male Adult

(n=107)

Female Adult (n=18)

Youth (n=36)

Male Adult

Female Adult Youth Overall

Pomegranate 0.1 0.9 0.0 0.0 120.0 - - 120.0Sugarcane 4.6 0.9 5.6 0.0 40.0 0.2 - 20.1Trees (multi-purpose) 1.1 38.3 38.9 25.0 14.0 1.6 98.7 34.1Trees (commercial) 1.1 2.8 0.0 2.8 - - - -Lucerne 0.8 0.9 0.0 0.0 1.0 - - 1.0Napier 0.3 0.9 0.0 0.0 9,600.0 - - 9,600.0Pasture 2.0 47.7 61.1 33.3 63.9 2.4 3.5 42.8

4.4 Marketing of outputs 4.4.1 Production and marketing of annual and perennial cropsMaize and Irish potato were used for both domestic consumption and as a source of income for the households (Table 4.28).

Table 4.28: Proportion (%) of crop produce (annual and perennial) marketed by households

Crop Adult male Adult female Youth AverageMaize 73.13 23.89 28.61 41.9Beans 97.85 29.11 48.50 77.88Irish Potato 59.74 48.48 16.8 41.7

4.5 Productivity of different types of livestock 4.5.1 Dairy productivity

Productivity of dairy animals in the county in the dry season is presented in Table 4.29. Milk was produced by local cattle, cross breed cattle, exotic cattle, local goats and exotic/dairy goats. The highest milk yield in MHHs was six litres obtained from exotic cattle. The FHH obtained a maximum of four litres from local and cross breed cattle while the youth obtained five litres from cross breed cattle. The MHHs reported three litres from exotic dairy goats while YHHs obtained two litres from local goats.


Table 4.29: Average milk production of different dairy animals during the dry season

Type of live-stock

Male adult HH Female adult HH Youth HH Overall

no. of animals

litres/animal/

day

no. of animals

litres /animal/

day

no. of animals

litres /animal/

day

no. of animals

litres / animal/

dayLocal cattle 6 3.5 3 4.3 2 3.4 5 3.6Cross breed cattle 2 3.9 2 4.2 2 5.1 2 4.1

Exotic cattle 4 6.4 -* - - - 4 6.4Local goats 3 1.9 - - 7 2.2 5 2.0Exotic dairy goats 1 3.0 - - - - 1 3.0

Local sheep 1 2.0 - - 11 1.6 9 1.7Camels 120 4.0 - - 8 20.0 64 12.0

*– data not available

In the wet season productivity of dairy animals increased compared to the dry season (Table 4.30). The FHHs obtained five litres from local and cross breed cattle while MHHs obtained about nine litres from exotic cattle. The YHHs obtained seven litres per day from cross breed cattle. The MHHs obtained five litres per day each from camels.

Table 4.30: Milk production by different dairy animals during the wet season

Type of livestock

Adult male HH Adult female HH Youth HH Overall

no. of animals

litres/ animal/

day

no. of animals

litres / animal/

day)

no. of animals

litres/ animal/

day

no. of animals

litres/ animal/

day Local cattle 6 4.7 3 5.1 2 4.1 5 4.6Cross breed cattle 2 4.0 2 5.0 2 6.8 2 4.6Exotic cattle 4 8.5 - - - 4 8.5Local goats 3 2.1 - - 7 2.5 5 2.2Exotic/dairy goats 1 5.0 - - - - 1 5.0Local sheep 1 - - - 11 0.5 9 0.5Camels 120 5.0 - - 8 3.0 64 17.5

4.5.2 Meat production Meat was produced from local cattle, cross breed cattle, local goats, local sheep, local/indigenous chicken, broiler chicken and rabbits (Table 4.31). The highest number of animals slaughtered in the last 12 months by MHHs was eight (rabbits with an average weight of 3.0 Kg). The FHHs slaugh-tered more local/indigenous chicken while YHHs slaughtered more local goats.


Table 4.31: Average meat production for different meat animals, by household head

Type of livestock

Adult male Adult female Youth Overallno.

slaugh-tered

on farm last 12

months

weight / animal

(kg)

no. slaugh-

tered on farm last 12

months

weight / animal

(kg)

no. slaugh-


months

weight / animal

(kg)

no. slaugh-


months

weight/ animal

(kg)

Local cattle 3.0 61.0 - - - - 3.0 61.0Cross breed cattle

2.0 120.0- - - -

2.0 120.0

Local goats 3.0 16.0 2.0 20.0 6.0 18.0 4.0 17.0Local sheep 3.0 16.0 2.0 18.0 5.0 19.0 3.0 16.0Local/indig-enous chicken

5.0 3.0 8.0 2.0 5.0 3.0 6.0 3.0

Improved indigenous chicken

6.0 3.0 1.0 3.0 - - 5.0 3.0

Broiler chicken 3.0 2.0 - - - - 3.0 2.0Rabbits 8.0 3.0 6.0 2.0 5.0 3.0 6.0 2.0

4.5.3 Egg production Table 4.32 presents the egg production of different types of poultry by gender of the household head. The highest egg production was within YHHs; was 50 eggs per cycle. Table 4.32: Egg production for different types of poultry

Household head

Type of livestockLocal/indigenous

chickenImprovedindigenous

chickenMale adult No. of layers 4 6

No. of eggs per hen per laying cycle 15 46No of laying cycles per year 3 4

Female adult No. of layers 5 6No. of eggs per hen per laying cycle 15 45No of laying cycles per year 3 5

Youth No. of layers 4 6No. of eggs per hen per laying cycle 15 50No of laying cycles per year 4 5

4.5.4 Manure production The average amount of manure produced in the last 12 months per household was 93 tons. Disaggregated by gender, the average amount of manure produced was 6, 56 and 501 tons for male, female and YHHs. The average use of manure per household was 0.4 tons in the last 12 months. By gender, men headed households used 0.4 tons, female headed used 0.8 tons and youth headed used 0.2 tons.


4.5.5 Apiculture production Only five households (3.1% of the surveyed households) were engaged in apiculture production. Disaggregated by gender, there were four MHHs (2.5%), and one YHHs (0.6%) engaged in apiculture. Traditional hives produced an average of 5.0 Kg in one year while improved hives produced 18 Kg of honey. None of the households reported to have harvested wax from either traditional or improved hives.

Honey production using traditional, improved and unconventional bee hives is presented in Table 4.33. The production of both honey and beeswax was similar for the traditional hives and improved beehives. In MHHs, improved hives produced about 150% more honey compared to traditional hives. Table 4.33: Honey and bees wax production and consumption

Hive product

Male Youth Overall meanHive type

Traditional Improved Traditional Improved Traditional Improved

Honey Produced (kg) 7.0 18.0 2.0 - 5.3 18.0

4.5.6 Hides and skins production The average number of hides produced was two in MHHs only. Average number of skins produced was one and three in male and YHHs respectively. An average of two hides was sold in MHHs. The average farm gate price was KES 100 for hides and KES 93 for skins.

4.5.7 Decision-making on use of proceeds from sale of various livestock products Decision-making on sale of milk was done by the household head and/or spouse in 92% of the cases (Table 4.34). Decision making on sale of eggs was mainly by the household head and spouse (87%). In MHHs, decision to sell eggs was mainly made by the spouse of the household head. In female and YHHs, the household head made most decisions on sale of eggs.

Table 4.34: Decision-makers on use of proceeds from sale of milk and eggs

Decision maker Proportion (%) of decision-making

Adult Male Adult Female Youth

Milk Eggs Milk Eggs Milk Eggs

Household head 40.3 38.6 60.0 50.0 41.7 54.5

Spouse of HH head 37.1 47.7 0.0 25.0 8.3 18.2

Joint HH and spouse 17.7 4.5 40.0 0.0 25.0 9.1

Male HH relative >35 years 4.8 9.1 0.0 12.5 16.7 18.2

Non-HH relative (female) 0.0 0.0 0.0 12.5 8.3 0.0 Eggs from all types of households were sold mainly to individual consumers (Table 4.35). There were no responses on where milk was sold.


Table 4.35: Sale of livestock products Where sold Proportion (%) of milk and eggs sold by HH

Adult male Adult femaleMilk Eggs Milk Eggs

Institutions-schools, hospitals, etc - 0.0 - 0.0Traders-brokers, hawkers - 13.9 - 12.5Individual consumers - 72.2 - 75.0Hotels - 13.9 - 12.5

- (dash) – no response

4.6 Contractual arrangements for marketing crops and livestock products 4.6.1 Contractual arrangements for crop marketing There were only four contract agreements made in crop marketing of which only one was formal while three were informal. Two informal contracts were made by YHHs and one in FHH. Crops with marketing arrangements were apple, maize, tomato and wheat. Figure 4.9 shows the differ-ent institutions the farmers had contracts with when selling their crops. Most popular institutions to enter into contracts with were co-operatives (61%) and traders (28%).

Figure 4.9: Actors involved in crop sale contractual arrangements

Thirty four percent of the contractual arrangements were formal while 66% were informal. The FHHs had no formal contracts while 37% of male and 29% of YHHs formal contracts. All households reported that contracts made on crop marketing in the county were with traders.

4.6.2 Contractual arrangement for marketing livestock and livestock productsForty one percent (n=66) of the respondents had established contracts with respect to sale of livestock products. From Table 4.36, 24% of households marketed milk through contracts while 3% did the same for eggs.


Table 4.36: Households with contractual arrangements for sale of livestock and livestock products

Livestock product

Proportion (%) of households having contractual arrangement Overall

(n=94)Adult male (n=70)

Adult female (n=2)

Youth(n=22)

Milk 38.0 28.6 33.3 24Eggs 8.0 14.3 0.0 5Manure 12.0 14.3 22.2 9Meat 6.0 14.3 11.1 5Live animals 36.0 28.6 33.3 23

4.7 Value addition of crops and livestock products 4.7.1 Value addition for different broad crop categories The main value addition practices for cereals were milling (84%), de-hulling (9%) and grading (8%), while grading (60%) and de-hulling (40%) were value addition practices in pulses. Grading and packaging were the main value addition practices in fruits and vegetables while grading and making chips were the value addition practices in root and tuber. Timber and firewood were the main value added to trees (Table 4.37).

Table 4.37: Value addition by type of household head

Crop categories Type of value addition Proportion of HH adding value

Cereals (n= 79 )Grading and/or packaging 7.6Making flour 83.5De-hulling 8.9

Pulses (n= 5)Grading and /or Packaging 60.0De-hulling 40.0

Roots and tubers (n=2)Grading/or packaging 50.0Chips 50.0

Vegetables (n=33)Grading and/or packaging 90.0De-hulling 3.0Drying 6.1

Trees(n= 36)Grading and /or packaging 2.8Timber 5.6Firewood 41.7Charcoal 19.4Posts 19.4Pegs 11.1

4.7.2 Value addition to livestock and fish products Forty six percent of households added value to milk by fermenting while 48% added value by boiling. A further 3% made yoghurt while only 2% cooled milk (Table 4.38). The main value addition practices in beef, mutton and chevron were drying and salting. Hides and skins were added value by drying and salting. De-feathering was the only value addition made on chicken. Value addition in chicken involved de-feathering (44%), differentiation of parts (34%) and packaging. Eggs were added value by boiling (56%), grading (11%) and packaging (8%) and as fertilized eggs (19%). The only value addition in honey was packaging.


Table 4.38: Value addition by categories of livestock and fish products

Livestock/fish product Type of value addition Proportion of HH adding value

Milk (n=59)

Fermenting 45.8Yoghurt 3.4Cooling 1.7Flavoring 1.7Boiling 47.5

Beef (n=10)

Preserving under fat 10.0Differentiation of parts 10.0Drying 50.0Salting 30.0

Goat meat (n=17)

Preserving under fat 11.8Differentiation of parts 11.8Drying 35.3Salting 35.3

Mutton (n=20)

Preserving under fat 25.0Differentiation of parts 10.0Drying 30.0Salting 30.0Smoking 5.0

Chicken (n=18)Dressing 94.4Boiling 5.6

Hides and skins (n=21)Salting 47.6Drying 52.4

Eggs (n=36)Boiling 55.6Packaging 8.3Fertilized eggs 19.4Refrigeration 2.8Grading 11.1


4.8 Employment and sources of household income 4.8.1. Farm income sources

Almost all households (93%) had only one source of income. Among the respondents, all FHH had only one source of income. Six percent of MHHs and 14% of YHHs had more than one source of income (Figure 4.10).

Figure 4.10: Income sources available to households

The main sources of household income included on-farm (crop and livestock sales, woodlot) and off-farm (salaried employment, on-farm and off-farm wages, businesses, remittances among others). Crop related on-farm income was earned by 32%, 28% and 31% of male headed, female headed and YHHs respectively. Income from livestock activities was earned by 59%, 56% and 67% of these respective households.

On-farm income earned the households an average of KES 204,370 with crop sources contributing the largest portion of this income (KES 200,142) (Table 4.39). Crop income represented 48% of all on-farm income compared to livestock’s contribution of 34% and 10% from pastures. Rent from pastures and fodder for MHH was KES 45,000 for just 0.9% of the households - all which were male headed - implying that the right to rent out land might be limited due to tenure arrangements where in a patriarchal society there is male dominance over the farm.

Table 4.39: Average annual household income from on-farm activities, by gender

Income sourceAdult male HH Adult female HH Youth HH Average

% KES % KES % KES KESCrop activity 31.8 259,306 27.8 102,140 30.6 61,818 200,142Livestock activity 58.9 163,923 5.6 57,034 66.7 116,057 140,677Woodlots 1.9 11,150 0.0 0.0 0.0 0.0 11,150Renting out pasture 0.9 45,000 0.0 0.0 0.0 0.0 45,000Other 4.7 19,990 16.7 16,800 11.1 7,000 14,825Total 248,585 99,040 124,763 204,370


4.8.2 Income from off-farm and non-farm activities About 14% of households were employed and derived their income from off-farm activities. Dis-aggregated by gender, 14% were MHHs, 3% women headed and 20% youth headed. In general, 11% of the male household heads earned their income from salaried employment where average income from this stream was KES 51,600 (Table 4.40). Farm wages by both spouse and the house-hold head were earned by about 4% of MHHs and 6% of YHHs earning an average of KES 37,600 over a period of one year. Income from business averaged KES 44,400 for about 11% of the total household sample where FHH earned KES 12,400 compared to youth earning KES 23,800 and MHH grossing KES 59,600 over the year from this source. The FHHs had three off-farm income sources compared to seven in youth headed and eight in MHH. Non-farm labor wages were the most common off-farm income sources for many households. This is followed by income from salaried employment where 11% of male, 25% YHHs had at least a member earning a salary.

Table 4.40: Average annual household income from off-farm and non-farm activities

Adult male HH Adult female HH Youth HH Overall KES/

year

% KES ‘000/year % KES ‘000/

year % KES ‘000/year KES ‘000/year

Salaried employment 11.2 51.6 25 74.4 60.0(13%)Salaried employment (spouse) 1.9 60.0 2.9 5.6 30.1 45.0(2.5%)Pension income 0.9 60.0 60.0(0.6%)Social protection 2.8 (0.6)Farm labour wages 3.7 38.2 5.6 36.5 37.6(3.7%)Non-farm labour wages 12.1 41.3 11.1 17.0 19.4 30.5 35.7(13.7%)Net income from business 10.3 59.6 11.1 12.4 13.9 23.8 44.4(11.2%)Amount from children 0.9 12.0 5.6 6.0 90.0(1.9%)Remittances from relatives 4.7 29.2 5.6 9.0 5.6 12.5 22.5(5.0%)Average 56,1 13.6 49.4 50,779

On average, off-farm income received by each household (based on total sample size) was KES 20,501 with salaried employment, business income and non-farm income constituting the highest proportion of the income (Table 4.41).

Table 4.41: Average off-farm household income

Off farm income categoryAverage amount (KES) earned per household head

Adult male (n=107)

Adults female (n=18)

Youth(n=36)

Overall(n=161)

Salaried employment 5,791 0 14,483 7,087Salaried employment (spouse) 561 0 838 560Pension income 561 0 0 373Farm labour wages 1,428 0 2,028 1,402Non-farm labour wages 5,028 1,889 5,944 4,882Net income from business 6,129 1,383 3,306 4,967Amount from children 112 0 167 112Remittances from relatives 1,364 500 694 1,118Average 20,974 3,772 27,460 20,501


4.9 Poverty and Vulnerability 4.9.1 Indicators of income and wealth One of the main aims of ASDSP is to alleviate poverty levels across the country and close the gender disparities in income. The total household income and gross wealth are indicators of the wellbeing of household members and ability of the household to meet its needs such as food, medical, school fees, agricultural inputs. They also provide an indication of how food insecure, poor or vulnerable a household could be.

Table 4.42 shows the mean values of some key indicators of income and wealth indicators by gender for the county. Total household income considers on-farm income from agricultural related activities within the farm, off-farm income (agricultural-related activities done outside the farm), and non-farm income (from non-agricultural related activities).

In addition to income, wealth considers the value of stocks, in this case livestock, household assets and savings which the household can fall back to in case of shocks or catastrophes. Livestock wealth was computed by multiplying all the numbers of all livestock of different species, ages and sex with their respective price, in case the farmer were to sell them at the time of the survey. The value of household assets was obtained by multiplying the number of assets owned by the household and the value they considered they would ask for in case they were to sell the item at the time of the survey. The household assets included housing structures (living houses, stores, sheds), household goods (furniture, radio, television), transport (vehicle, bicycles, motorbikes), agricultural equipment (threshers, chaff-cutters, ploughs), other household infrastructure (such as boreholes) for distribution of assets owned. About 36 different household assets were identified in the county (Annex 2). Land is also an important indicator of wealth but it was not valued in this particular study.

The mean value of total household income for Laikipia County was KES 286, 888 while the mean value of gross wealth was KES 1,583,817. The annual per capita income and per capita gross wealth were KES 71,722 and KES 395,954, respectively. The mean daily per capita income was KES 196. The mean per capita income for MHH was KES 190, while for the female headed and YHHs was KES 108 and KES 131 respectively. This shows a big disparity of per capita income by gender of head of household.


Table 4.42: Mean values of various indicators of income and wealth, by gender of household head

Indicator

Indicator valueMean value

(N=161)Adult male

(N= 107)

Adult female (N=18)

Youth (n=36)

1. Household size (no.) 5 3 4 4

2. Land size (acres) 3.5 3.4 4.7 3.4

3. Per capita land size (Line 2÷Line 1) 0.7 1.13 1.17 0.85

4. Value of all livestock owned (KES) 1,039,434.9 151,305.3 409,576.0 793,453.0

5. Value of household assets (KES) 589,098 584,322 210,944 503,476

6. Total on-farm income (KES) 248,585 99,040 124,763 204,370

7.Total off farm income (KES) 56,105 13,580 49,428 50,779

8. Livestock off-take (4% of value of livestock) 41,577 6,052 16,383 31,738

10.Total household income (Lines 6, 7, 8 and 9) KES 346,267 118,672 190,574 286,888

11.Annual per capita income (Line 10÷Line 1) KES 69,253 39,557 47,643 71,722

12.Daily per capita income (Line 11÷365 days) KES 189.7 108.4 130.5 196.513.Gross household wealth (Sum of lines 4.5,and 10)

KES 1,974,800 854,299 811,094 1,583,817

14. Annual per capita gross wealth per (Line 13÷Line 1) KES 394,960 284,766 202,774 395,954

4.9.2 Wealth and other socio-economic indicators by vulnerability The sample households were categorized into vulnerable and non-vulnerable ones using the level of total income as a proxy indicator of vulnerability. After estimating the total income and order-ing the households from the highest to the lowest income earners, proportions of poor households gathered from the Commission of Revenue Authority’s fact data on counties were used to estab-lish the borderline between the vulnerable and non-vulnerable. For instance, for Laikipia County, the poverty rate was 50.5% implying that the same proportion of the households, counted from the one with the lowest income, are treated as vulnerable. This approach gave 66% vulnerable and 33.8% non-vulnerable sample households.

Several indicators or parameters provided a clear distinction between vulnerable and non-vulnerable households in terms of socio-economic wealth and capacity to cope with shocks and poverty. Table 4.43 indicate that non-vulnerable households possess/access more productive resources that make them better off when adapting or coping with shocks.


Table 4.43: Wealth and other socio-economic indicators by vulnerability

Wealth/socioeconomic parameterVulnerability

Overall (n= 130 )Vulnerable (n=86)

Non-vulnerable (n=44 )

Household size (No) 4 4 4Land size (acres) 3.3 4.3 3.7Value of livestock owned (KES) 105,056 1,420,853 793,453Total on-farm income (KES) 22,209 319,157 204,370Off-farm non-farm income (KES) 23,068 64,957 50,779Value of household assets (KES) 373,642 630,104 503,476Proportion (%) of HH with agricultural savings (%) 4.9 18.8 11.8

Climate shocks (% experiencing) 87.5 88.9 88.2Social protection (%receiving) 6.3 7.4 6.8% of people employed on-farm 5.0 2.5 3.7% of people employed off-farm 11.3 16.0 13.7Credit (% accessing) 1.3 1.2 1.2Hire-in labour (%) 72.5 63.0 67.7Hire-out labour (%) 25.0 22.2 23.6

4.10 Food and nutrition security Food security is defined as a state whereby, an individual, household, national, regional and global levels, “all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life” (FAO, 1996). The critical issues in the food security definition focus on availability, access, and utilization of food among the people. Food security thus involves dimensions that encompass food production, distribution and marketing, preparation, processing and storage as well as issues to do with population and health, education, employment and income. It also requires involvement not just of the households but also the national governments and the international community. Household food security refers both to the availability and to stability of food, together with purchasing power of the household. This section entails analysis of food and nutrition security situation among the households in Laikipia County specifically with a view of determining the level of food insecurity, nutritional status and nutrient intakes among both the rural and urban populations in the county.

4.10.1 Food production, availability and seasonality

As shown in the above definition, food production is one of the indicators of food security. In addition to production, there are other factors that influence food and nutrition security: Post-harvest losses; household size (which for the county was estimated at four (4) members per household); food availability (number of meals per day); and seasonality in food supply.

On average, individual members of the household (all the different age groups and gender) ate three meals a day on a normal day during peak food availability. In the low food availability season, the children took two and a quarter while other age groups took two meals per day. The overall proportion of households did not have enough food to meet their needs was 80%. At least 89% of male-, 94% of female- and 78% of youth HH did not have enough food to meet their needs in the year of study.


4.10.2 Seasonality in food supply

Seasonality in food production has an influence on food security. Figure 4.11 gives the proportion of farmers who indicated non-availability of enough food at household level over months. The results show that there is distinct peak of food scarcity in the county. This occurs in July to December. This indicates food insufficiency in half the year. In food deficit months, a higher proportion of FHH were food insecure compared to the MHH, with at least 70% of FHH being food insecure in July to December.

Figure 4.11: Proportion of households that were food insecure over the 12-months of the study period

The peaks and lows in food availability are indicators of food and nutritional security. One week’s recall memory was used to evaluate the food availability. Table 4.44 shows that within a period of one week, 20% male, 11% female and 14% YHHs experienced low food availability. The normal food availability was recorded by 60% households. Almost all households, between 20% and 33% of the households regardless of gender of household head, experienced peak food availability.

Table 4.44: Peak and low season food availability in the county

Season% availability response by household head

Adult male (n=94) Adult female (n=18)

Youth (n=35)

Overall (n=147)

Peak food availability 21.3 33.3 20.0 22.4Low food availability 20.2 11.1 14.3 17.7Normal food availability 58.5 55.6 65.7 59.9

4.10.3 Food and nutrition security index

The problem of malnutrition is attributed to poor diversification of food sources eaten in households and can lead to malnutrition, and stunted growth among others. Food quantities consumed at the household level were computed using a structured questionnaire for data collected from primary source. The foods consumed were converted to calories using the available food consumption tables. Resulting calorie values were divided by the number of Adult Equivalent (AE) in the household, in order to obtain numbers that are comparable across households of different size. A food secure household is defined as one whose calorie supply per AE is greater than or equal to the


minimum daily calorie requirement for adult of 2,260 kcal. Households with lower calorie intakes are considered to be food insecure. i)DietarydiversityoftherespondentsA one-week recall period was used as a reference period to measure household dietary diversity (a proxy for quality of diet) as shown in Table 4.45. The following set of 12 food groups were used to calculate the household dietary diversity: • Cereals

• Root and tubers• Vegetables • Fruits • Meat, poultry, offal• Eggs• Fish and seafood • Pulses, legumes and nut • Milk and milk products • Oil/fats • Sugar/honey • Miscellaneous foods

The mean distribution of the dietary diversity scores out of a maximum of 12 was 1.80 for adult male-, 1.68 for adult female- and 1.75 for youth-headed households (Table 4.45).

Table 4.45: Mean of dietary diversity score/index

Household category Dietary Diversity Score level Mean score SEAdults male HH (n=107) Low food diversity (max 2 food groups) n=56 1.06 0.08

High food diversity (min 3 food groups n=51 2.62 0.07

Adult female HH (n=18)Low food diversity(max 2 food groups) n=10 1.07 0.19High food diversity (min 3 food groups n=8 2.45 0.16

Youth HH (n=36)Low food diversity(max 2 food groups) n=16 0.68 0.15High food diversity (min 3 food groups) n=20 2.62 0.11

Average adult male HH (n=107) 1.80 0.09Average female HH (n=18) 1.68 0.21Average youth HH (n=36) 1.75 0.18

The male headed respondents had a marginally higher score than youth and female headed ones. The percentage response for low diversity group was higher than high diversity ones for male and FHH but the reverse was true for YHHs (Table 4.46). The implication is that about 51% of the population of the county rural farmers studied consumed less than three food groups within one week.

Table 4.46: Distribution of respondents by dietary diversity score/index

Gender category% response

low food diversity (#2 food groups) High food diversity Medium (>3 food groups)

Female headed (n=36) 55.6 44.4Male headed (n=107) 52.3 47.7Youth headed (n=36) 44.4 55.6Overall (n=161) 50.8 49.2


ii)HouseholdFoodSecurityStatus

The apparent disparity between the rate of food production and demand for food in Kenya has led to a food deficit and hence posing a threat to national food security. The number of man-equivalent units was estimated for the total population and according to the socio-demographic variables in the county. The average calorie per man unit per day were, 32,382, 28,383 and 27,424 for male adult, female adult and YHHs respectively with an overall mean of 30,916 (Table 4.47). Table 4.47: Mean calorie intake per man-equivalent, 2013

Gender category Food security categoryCalorie intake per man-equivalent

Mean sd

Adult male HHFood secure n=81 38,170 8,207

Food insecure n=15 1,127 204

Adult female HH Food secure n=16 30,146 8,688

Food insecure n=1 179 1

Youth HH Food secure n=24 31,830 7,177

Food insecure n=4 992 246

Food secure n=121 35,852 5,776

Food insecure n=20 1,053 165

Male headed and managed households n=96 32,382 7,055

Female headed and managed households n=17 28,383 8,349

YHHs n=28 27,424 6,475

Pooled n=141 30,916 5,059

Compared with the standard level of 2,260 kcal, the households, on average 24% were food insecure. About 24% male and 11% FHH were food insecure compared to 33% YHHs (Table 4.48).

Table 4.48: Distribution of food secure and insecure households

Food security category%response

FHH (n= 18) MHH (n=107) YHH (n=36) All (n=161)Food insecure 11 24 33 24Food secure 89 76 67 76

4.11 Collective action

4.11.1 Membership of household members to agricultural groups

Respondents were asked whether any of their household members belonged to any agricultural activities related groups or associations. About 6% of HHs sampled indicated they had household members who belonged to some groups and 97% of these indicated the groups they belonged to were registered. In terms of the gender of the household members who belonged to groups, 18% were adult males, 11% were adult females and 11% youths as shown in Figure 4.12.


Figure 4.12: Membership to groups by household members

4.11.2 Types and categories of groups

Household members belonged to four main types of groups: 59% to co-operative society, 18% to producer, 12% to processing and 7% producer and marketing (Figure 4.13).

Figure 4.13: Types of agricultural group members belonged


In terms of gender, the mixed group had the highest membership (93%) where there were approximately equal men and women in the groups (Figure 4.14).

Figure 4.14: Category of the groups

4.11.3: Main commodities and activities of the groups

Of the available groups, 81% dealt with livestock related activities while 18% dealt with crop related activities (Figure 4.15). The main activities of the groups were produce marketing (70%), processing 22% and savings and credit (7%).

Figure 4.15: The main commodities the groups dealt with


4.12 Access and satisfaction with various services

4.12.1 Access and satisfaction with support services and infrastructure

The two most accessed support services were infrastructure (95%) and agriculture (42%) services (Figure 4.16). Only one percent accessed financial services while less than 7% reported having accessed climate services.

Figure 4.16: Access to support services

4.12.2 Access, use and satisfaction with credit

From a sample size of 161, only 1.2% of male- and none in female- or youth-headed households obtained agricultural credit in the last 12 months. The mean amount borrowed was KES 27,500 and that those accessing credit were satisfied. 4.12.3 Access and satisfaction with market information Of the sampled households, 29% obtained market information in the last 12 months: 19%, 3% and 7% of male, female and YHHs respectively). About 47% were satisfied with market information they received as shown in Table 4.49.

Table 4.49: Level of satisfaction (%) with market information services

Gender Dissatisfied Neutral SatisfiedMale 25.5 29.8 42.6Female 4.3 2.1 4.3All 29.8 31.9 46.8


4.12.4 Access, use and satisfaction with formal savings services Twelve percent (n=161) of the respondents had access to formal savings services. Access to services by gender was 9% for adult MHH, 1.2% for adult FHHs and 1.2% for YHHs respectively (Figure 4.17).

Figure 4.17 Household members who accessed savings services

4.12.5 Access, use and satisfaction with insurance servicesNone of the respondents reported having accessed insurance services. 4.13 Climate change challenges, adaptation and coping strategies

4.13.1 Sources of climate related information

About 96% of sampled respondents had obtained climate related information, disaggregated as 95%, 100% and 97% for adult male, adult female and YHHs. The main sources of climate related information were traditional indigenous knowledge (41%), radio (38%) and television 11% across gender (Figure 4.18). Government extension, faith based organizations (FBOs), meteorological department and internet were sources of information to less than 5% of respondents.

Figure 4.18: Sources of climate related information


4.13.2 Respondents noticing long term environmental changes About 91% of households had noticed changes in long-term environmental trends; this did not vary across gender, (91% of male headed, 100% of female headed and 97% of youth headed). These long-term trends included deforestation (62%), drying up of wells (61%) and reduction in water volumes (43%). Proportionally, more adult males identified soil degradation and emergence of new plants and animals as a consequence of climate change than other gender categories. Adult females compared to other gender, identified reduction in water volumes, disappearance of some plants and animals and incidence of new pests and diseases as key indicators of climate change (Table 4.50).

Table 4.50: Awareness of households about long term environmental changes %)

Environmental changes Male adults Female adults Youth N=161

Deforestation 62.9 55.6 62.9 62.0Drying of wells and rivers 60.8 44.4 71.4 61.3Reduction in water volumes 40.2 55.6 45.7 43.3Soil degradation 20.6 16.7 8.6 17.3Incidences of new diseases and pests 7.2 11.1 8.6 8.0Disappearance of some plants and animals 3.1 11.1 2.9 4.0Emergence of new plants/animals 2.1 0.0 0.0 1.3Land slides 1.0 0.0 0.0 0.7Overall 90.6 100 97.2

4.13.3 Types of adaptation strategies to climate change

The main adaptation strategies used were water harvesting (43%) and tree planting (42%). Other adaptation strategies were changing the crop type (29%), increased irrigation (17%) and seeking employment (22%). The least adopted strategies were diversification of enterprises (1.9%), leasing land (2.5%), communal seed banks (4.3%) and food storage structures (5.6%), (Table 4.51).

Table 4.51: Strategies for adapting to climate change

Adaptation strategy Adult male Adult female Youth OverallWater harvesting 47.7 38.9 33.3 43.5Tree planting 43.0 55.6 33.3 42.2Changing crop type 29.0 33.3 25.0 28.6Staggered cropping 29.9 16.7 25.0 27.3Increased soil and water conservation 23.4 22.2 30.6 24.8Seeking employment 21.5 27.8 19.4 21.7Changing livestock type 20.6 5.6 19.4 18.6Increased irrigation 16.8 22.2 16.7 17.4Value addition 13.1 33.3 5.6 13.7Feed conservation and diversification 13.1 5.6 16.7 13.0Food storage structures 5.6 0.0 8.3 5.6Communal seed banks 3.7 5.6 5.6 4.3Leasing land 3.7 0.0 0.0 2.5Diversification of enterprises 1.9 0.0 2.8 1.9


4.13.4 Training on climate change strategies Only 12% of respondents had been trained in climate change strategies.The MHH constituted 2% while female and YHHs were 17% and 14% respectively. Adult males trained were 21.5% while adult females and youth constituted 16.7% each. Only three youth had received training on adaptation strategies. Both male and FHHs had almost equal training in climate adaptation (Table 4.52).

Table 4.52: Household members trained in adaptation to climate change

Adaptation strategies trained onProportion (%) of household heads trained

Overall(%) Adult male

(n=107)Adult female

(n=18)Youth(n=36)

Increased soil and water conservation 3.7 0.0 2.8 3.1Tree planting 3.7 5.6 0.0 3.1Water harvesting 2.8 5.6 0.0 2.5Change crop type 3.7 0.0 0.0 2.5Change of livestock 1.9 0.0 5.6 2.5Seek employment 0.9 0.0 5.6 1.9Value addition 1.9 0.0 0.0 1.2Food storage facilities 0.9 5.6 0.0 1.2Increased irrigation 2.8 0.6Feed conservation and diversification 0.9 0.0 0.0 0.6Staggered cropping 0.9 0.0 0.0 0.6Overall (out of total) 21.5 16.7 16.7 19.9

4.13.5 Types of climate shocks experienced At least 88% of households - disaggregated as 89%, 94% and 83% respectively for adult males, adult females and YHHs - experienced at least one climate shock in their agricultural production activities within the study year. The most common shocks experienced were droughts (83%), poor rain distribution (74%) and livestock diseases (65%). Increased droughts, human pest and diseases and floods were other reported shocks experienced in the study area (Figure 4.19). Other shocks were landslides, hailstorms and frost attack

Figure 4.19: Climate shocks experienced


4.13.6 Coping strategies to climate change Among those who experienced the climatic shocks, 88% used at least one coping strategy. Disaggregated by gender, 83% of male households, 19% of female headed and 88% of YHHs used at least one coping strategy. The main strategies were purchasing food, increasing frequency in pest and disease control in crops and livestock and temporary migration to other areas. Table 4.53 shows selected strategies that were used by the respondents.

Table 4.53: Coping strategies to climate change

Coping strategies usedGender of HH members trained (%)Adult male

Adult female Youth Overall

Purchased food 24.3 24.6 20.2 77.6Increased use/frequency in crop and livestock pest and disease control 18.4 13.1 16.8 57.8

Temporary migration to other areas 10.7 8.2 12.6 36.0

Sold/slaughtered livestock to access food 4.5 3.3 6.7 16.1

Sort off-farm employment 2.8 8.2 1.7 10.6

Increased watering intervals to livestock 2.3 4.9 1.7 8.1

Used previously stored food 1.4 0.0 4.2 6.2

Relied on traditional support systems 1.7 0.0 2.5 5.6

Destocking to reduce risks 1.1 0.0 4.2 5.6

Sold assets 2.3 0.0 1.0 5.5

4.13.6.1Householdmemberswhorespondedtoclimateshocks

Sevety seven (77%) of households responded to climate events that significally affected household income (climate shocks). Disaggregated by gender, 78% adult male, 89% adult female and 69% youth responded to climate shocks. As indicated in Table 4.54, the main reponses were in response to droughts (83%), poor distribution of rain (74%) and livestock pests and diseases (65%).

Table 4.54: Response to climate related shocks

Climate shockGender of person that responds to climate shocks (%) Overall

(n=161)Male (n=107) Female (n=18) Youth (n=36)

Droughts 83.2 94.4 77.8 83.2

Poor rain distribution 71.0 88.9 75.0 73.9

Livestock Pest and Disease 67.3 55.6 63.9 65.2

Crop Pest Disease 54.2 77.8 41.7 54.0

Human Parasite Diseases 30.8 22.2 44.4 32.9

Floods 23.4 33.3 41.7 28.6

Frost 5.6 0.0 2.8 3.1

Landslides 1.9 0.0 2.8 1.9


4.13.6.2Capacityofhouseholdstocopewithclimatechange Overall household capacity to cope with climate changes was classified as high (32%), neutral 48% and low 20%. In MHH, the capacity was 27%, 52% and 21% for high, neutral and low respectively. In FHHs, it was 42%, 37% and 21% respectively. In YHHs, capacity to cope with shocks was 41%, 41% and 18% for high, neutral and low capacity respectively. Capacity to respond to the shocks was high in response to landslides (68%), but generally capacity to respond to most of the shocks experienced was low (Table 4.55). This calls for intervention in improving access to early warning systems especially in case of floods, hailstorms and frost. To mitigate poor rain distribution and drought shocks, investments can be made in water conservation, irrigation and drought tolerant crops.

Table 4.55: Capacity of households to respond to the climate shocks)

Major climate shock experienced Capacity to respondProportion of respondents by gender of HH

Male Female Youth OverallCrop Pest Disease High 23.2 38.5 28.6 26.5

Neutral 46.4 30.8 35.7 42.2

Low 30.4 30.8 35.7 31.3Livestock Pest Disease High 34.3 37.5 54.5 39.2

Neutral 49.3 37.5 40.9 46.4

Low 16.4 25.0 4.5 14.4Droughts High 22.2 40.0 40.7 28.5

Neutral 65.2 40.0 33.3 49.6

Low 11.6 20.0 25.9 22.0Poor Distribution Rain High 23.2 40.0 36.4 23.8

Neutral 65.2 40.0 50.0 58.5

Low 11.6 20.0 14.6 13.2Human Parasite Diseases High 48.3 75.0 46.2 50.0

Neutral 27.6 25 46.2 32.6

Low 24.1 0 7.7 17.4Floods High 8.7 40.0 33.3 20.0

Neutral 47.8 40.0 41.7 45.0

Low 43.3 20.0 25.0 35.0Landslides High 50.0 0.0 100.0 66.7

Neutral 50.0 0.0 0.0 33.3Hailstorms High 33.3 0.0 0.0 33.3

Neutral 66.7 0.0 0.0 66.7Frost High 20.0 0.0 0.0 16.7

Neutral 80.0 0.0 100..0 83.3


4.14 Natural Resource Management practices The study examined respondents’ knowledge and practices in NRM. The main practices included minimum tillage, crop rotation, intercropping, mulching, cover-cropping, terracing and planting pits. 4.14.1 Proportion using agroforestry and types of agroforestry practices used About 40% of all households practiced agroforesty in their farms. Disaggregated by gender, 38% adult male HH, 61% adult female HH and 33% youth HH practised agroforestry on their farms. Among those who practised agroforestry, wind breaks (37%) along the boundaries and shade trees (32%) were the most commonly used methods (Table 4.56).

Table 4.56: Households practising agroforestry technologies

Agroforestry practiceMale headed

managed house-holds

Female headed managed

households

Youth headed managed

household

Overall (%)

(n=161)

Improved fallows 2.5 5.0 0.0 1.8

Alley cropping (trees within crops) 6.3 5.0 4.3 4.3

Shade trees 41.3 40.0 47.8 32.3

Windbreaks (along boundaries) 48.8 50.0 47.8 37.3

Silvo-pastoral (trees with livestock/pasture) 1.3 0.0 0.0 0.6

4.14.2 Main natural resource management practices known and used Only about 50% of all household sampled knew about NRM practices, disagregatted as 54% of adult male, 36% of adult female and 47% of youth HH respectively. Households practicing at least one form of NRM were 40%; dissaggregated by gender, they were 43%, 27% and 38% of adult male, adult female and youth HH respectively. Major NRM practices known and practised were planting pits and intercropping while the least adopted were mulching, minimum tillage and terracing (Table 4.57).

Table 4.57: Knowledge and practices of natural resource management

Resource Management Practice

Male Headed (n=107)

Female Headed (n=18) Youth Headed

(n=36) Overall (n=161)

Know Practice Know Practice Know Practice Know Practice

Minimum tillage 39.3 37.4 44.4 50.0 33.3 19.4 38.5 34.8

Crop rotation 58.9 43.9 83.3 50.0 55.6 41.7 60.9 44.1

Intercropping 87.9 57.0 88.9 72.2 72.2 55.6 84.5 58.4

Mulching 43.0 28.0 5.6 38.9 41.7 25.0 44.1 28.6

Cover cropping 53.3 34.6 66.7 44.4 55.6 41.7 55.3 37.3

Terracing 57.9 35.5 66.7 44.4 50.0 30.6 57.1 35.4

Planting pits 86.9 64.5 77.8 72.2 86.1 55.6 85.7 63.4


5.0 CONCLUSION AND RECOMMENDATIONS The study depicted a low level of tertiary education, where a very small proportion (2% of the households). Household income was mainly on-farm, with a very small portion from the off-farm category. The MHH had higher income, used more inputs and made more decisions in the market-oriented agricultural commodities than the female house hold heads. The major constraints to input use in crop and livestock production were high prices and a long distance to the market. Productivity of annual and perennial crops in both Season 1 and 2 was low which could be attributed to low usage of production inputs, low access of agricultural services and low mechanization of farm activities. There was low use of input and rudimentary value addition methods. A high proportion of households did not have enough food to eat, with 66% of the households considered as vulnerable. Non-vulnerable households possess/access more productive resources that make them better off when adapting or coping with shocks. Awareness level on NRM technologies was high but implementation was low.

To increase and sustain agricultural productivity the extension arm of the county government in partnership with the private sector should:

• Develop and implement plans/programmes that ensure farm inputs are available and farmers are sensitized on their use.

• Enhance service provision to farmers, especially financial services that can help them increase crop and livestock productivity, market information to help them market their produce, extension services to bring current technologies to farmers, among other services.

• Promote value addition by investing in infrastructure, capacity building and provision of relevant technologies.

• Diversify food production including high value and nutritious foods both for home consumption and sale.

• Promote NRM to conserve the resource base and also adapt to and mitigate against climate change. This should be done through raising the level of awareness among value chain actors on NRM and climate change issues, enhancing the use of climate smart technology/NRM inputs and advisory services particularly for vulnerable groups, and promoting equitable engagement in local NRM/CC planning.

• Improve access to social protection and security services by vulnerable groups through supporting the provision of basic socio-economic services to enable the resource poor and vulnerable producers to uplift their productive capacity to a level that will allow them to engage in commercial production

• Enhance community action capability through support for establishment and functioning of community groups


6.0 BIBLIOGRAPHYClaro, R.M., Levy, R. B., Bandoni, D. H., Mondini, L.,. 2010. Per capita versus adult-equivalent estimates of calorie availability in household budget surveys. Cad. SaúdePública, 27:2188-2195.

Devereux, S. 2003. Policy Options for Increasing the Contribution of Social Protection to Food Security.Institute of Development Studies University of Sussex.

FAO. 1996. Rome Declaration on World Food Security and World Food. Food and Agricultural Organization. Rome, Italy.

GoK (2010). 2009 Population and Housing Census Results. Nairobi: Kenya National Bureau of St, Ministry of Planning, National Development and Vision 2030.

GOK (2012).Agricultural Sector Development Strategy (2010 – 2020). Nairobi: Ministry of Agriculture

GOK, Ministry of Devolution and National Planning (2013) County Development Plans.

Laikipia CDP, 2013. Laikipia County Development Profile, 2013

Ministry of Agriculture (2012) Economic Review of Agriculture.

Onyiriuka, A.N.,Fmcpaed; M., Umoru, D. D., Ibeawuchi, A. N.,. 2013. Weight status and eating habits of adolescent Nigerian urban secondary school girls. S Afr J CH 7:108-112


7.0 ANNEXES

Annex 1: ASDSP logical framework - County Baseline Indicators

Outcome Indicators Baseline data needed Level of indicator

1. To increase equitable incomes, Employment and food security of both male and female target groups as a result of im-provedproduction and produc-tivityin the smallholder farm and off-farm sectors

On-farm income increase by 5% p.a. in both male and female-headed households by 2017

Current total mean on-farm income (Disaggre-gated by gender; male, female, youth headed)

• Overall mean value = KES 204,370

• Mean MHH annual on-farm income = KES 248,585

• Mean FHH annual on-farm income = KES 99,040 and

• Youth KES = 124,763

Off-farm income increase by 6% p.a. in both male and female-headed households by 2017

Current total mean off-farm income (Disaggre-gated by gender; male, female, youth headed)

• Overall mean annual value=KES 507791

• Mean MHH annual off-farm income = KES 56,105

• Mean FHH annual off-farm income = KES 13,580

• Mean FHH annual off-farm=49,428

2. To increase equitable income, Employment and food security of both male and female target groups as a result of improved production and productivity in the small-holder farm and off-farm sectors

Food and nutri-tion security level increase by 10% in both male and female-headed households by 2017

- Current level of food and nutrition security for male and FHHs:

- Need to establish food and nutrition security

Food insecurity • Overall households food in-

secure= 24% of households: • Male headed food insecure

households = 24% • Female headed insecure

households = 11% • Youth headed insecure

households=33%

Productivity for major food com-modities increase by 10% by 2017

- Current productivity (yields kg/acre) of maize –the year. (Overall, and disaggregated gender male and female headed)

Maize• Overall mean for the

year=821kg /acre,• Male headed household for

year=789kg/acre • Female headed (FHH) for

year=538 kg/acre

• Youth headed (YHH) for year=1177 kg/acre:

- Current productivity (yields kg/acre) of beans per year. (General, and disaggregated by gender male and female headed)

Beans• Overall=268 kg/acre. • Male headed=256 kg/acre • Female headed (FHH)=337

Kg/acre• Youth headed (YHH)=247

Kg/acre


Outcome Indicators Baseline data needed Level of indicator- Current productivity (yields) of major live-stock species (General, and disaggregate by gender male and female headed)

Milk (litres/day): Crossbred (litres/cow/day)• Overall=4;• MHH=4;• FHH=4.6; • YHH=6.

Exotic cattle: (litres/cow/day)• Overall=7.5;• MHH=7.2;• FHH=nil; • YHH=nil.

Local poultry (eggs/hen/laying cycle)• Overall =248; • MHH=180; • FHH=325; • YHH=240

3. To increase equitable incomes, employment and food securityof both male and femaletarget groups as a result of improved production and productivity in the smallholder farm and off-farmsectors

Household asset index for women, youth and vulner-able groups in-creased

- Current household asset wealth overall, by Gender (women, youth and vulnerable groups) - Need to establish asset ownership wealth by gender (women, youth and vulnerable groups)

• Overall Mean household asset value= KSh503,476,

• MHH = KES 589,098, • FHH = KES 584,322, • YHH=KES 210,944

VulnerabilityOverall=KES 503,476Vulnerable=KES 373,642Non Vulnerable=KES 630,104


Outcome Indicators Baseline data needed Level of indicator4. Environmental resil-ience and social inclusion in value chains strengthened

% increase HH who notice long-term changes in the environment

- Current % of HH re-porting noticing changes in environment: Overall, by gender.

• Overall mean report-ing=91%

• MHH=91%, • FHH=100% and • YHH=97%

% increase of HH experiencing climate shocks in their agricultural activities

Current % households experiencing climate shocks: Overall and by gender

• Overall mean report-ing=88%

• MHH=89%• FHH=94%• YHH=83%

C2 (a) % increase in households responding to cli-mate-related risks has improved, by gender and vul-nerability

- Current % of house-holds responding to cli-mate shocks and :Over-all and by gender and vulnerability (Perception therefore proportions by gender)

Overall mean = 77%,• MHH= 78%,• FHH=89%• YHH=69%HH Capacity to respond: Overall: High=32, Neutral=48, Low=20MHH: High=27, Neutral =52, Low =21FHH: High=42, Neutral = 37, Low =21, YHH: High=41, Neutral = 41, Low =18


Outcome Indicators Baseline data needed Level of indicator5. Value chain develop-ment. Viable and equi-table commercializationof the agricultural sector promoted

C3 (a) Proportion of output mar-keted by major agricultural com-modities increased by 10% by 2017

Maize: Overall 41.9%• MHH= 73.1%,• FHH=23.9%,• YHH=28.6%

Irish potato: Overall 41.7%• MHH= 59.7%,• FHH=48.9%,• YHH=16.8%

Beans: Overall 78%• MHH= 98%,• FHH=29%,• YHH=48%Milk: Overall 24%• MHH=38%,• FHH=29%• YHH=48%Beef: Overall 2%• MHH=2%,• FHH=0%• YHH=0%

On farm employ-ment increase by 5% p.a. by 2017, disaggregated by gender and vul-nerability

- Current % of people employed on farm and disaggregated by gender

• Overall proportion=83%,• MHH=81.6%,• FHH=88.8% and • YHH=73.6%

VulnerabilityOverall=3.7%Vulnerable=5.0%Non Vulnerable=2.5%

Off farm employ-ment increase by 6% p.a by 2017, disaggregated by gender and vul-nerability

- Current % of people employed off farm dis-aggregated by gender

• Overall proportion=17%,• MHH=18.4%, • FHH=11.2% and • YHH=26.4%

VulnerabilityOverall=13.7%Vulnerable=11.3%Non Vulnerable=16.0%


Outcome Indicators Baseline data needed Level of indicatorComponent 1.3: Linkages betweenkey sectors stakeholders (Programmes, researchers, educational institutions, extensionists and Vc ac-tors) improved

1.3.1.2.% increase in proportion of Producers access-ing public and or private agricul-tural services and infrastructure (by type)

- Current % of farm-ers accessing public agric services and infrastructure by type - Current % of farmers accessing private agric services and infrastruc-ture by type

• 85% of households mainly access agricultural-related services (public and pri-vate).

• 40% of all households mainly accessing from public institutions and 60% from private institutions.

• 38% of MHH mainly ac-cessing public institutions and 62% MHH accessing private institution

• 35% of FHH mainly access-ing private institutions and 65% FHH accessing private institution

• 47% of YHH mainly access-ing public institutions and 53% YHH accessing private institution


Outcome Indicators Baseline data needed Level of indicator2.2.1 % change in productive asset (Land, Labour and capital) access, disaggregated by gender and vul-nerability

- Current % access to productive assets (Land, Labour, capital) by gen-der and vulnerability

• 29% of households accessed market information servic-es, 19% of MHH, 3% FHH and 8% YHH.

• 1.2% of households ac-cessed credit, 1.2%of MHH, 0%FHH and 0% YHH.

• 12% of households accessed saving services, 9% MHH, 1.2% FHH and 1.2% YHH.

• None of the households accessed agricultural insur-ance

• 6.2% of households ac-cessing agricultural tech-nologies, 4.7% MHH, 16.7% FHH and 5.6% YHH.

• 6.2% of households ac-cessed social protection, 7.5% MHH, 0% FHH and 5.6% YHH

Vulnerability• 11.8% of households ac-

cessed saving services, 4.9% vulnerable, 18.8 % Non vulnerable

• 1.2% of households ac-cessed credit, 1.3%vulner-able ,1.2%Non vulnerable

• 6.8% of households ac-cessed social protection, 6.3% vulnerable ,7.4% Non vulnerable


Annex 2: Respondents owning different household assets in the County (%)

Item No. Item Percent of respon-

dents (N=161) Item No. Item Percent of respon-dents (N=161)

1 Radios 78.9 22 Cattle dip 0.62 Telephone Mobile 83.9 23 Car 1.23 Zero grazing Units 1.2 24 Chaff Cutter 0.64 Television sets 33.5 25 Truck lorry 1.25 Bicycles 43.5 26 Piggery Houses 1.26 Water Tanks 37.9 27 Borehole 1.27 Wheel Barrow 24.8 28 Silage Pit 1.98 Stores 26.7 29 Power Saw 1.29 Spray Pump 44.1 30 Well 0.610 Solar Panels 30.4 31 Posho Mill 0.611 Battery Car 31.1 32 Generator 3.112 Water Trough 8.7 33 Boom Sprayer 0.613 Gas Cooker 7.5 34 Green House 0.6

14 Motorcycle 13.0 35 Desk Top Com-puter Laptop 1.2

15 Hand Cart 36 Dams 3.716 Water Pump 14.317 Weighing Machine 12.418 Donkey Cart 1.219 Beehives 1.9

20 Sewing Knitting Ma-chine 3.1

21 Poultry houses 1.2


AGRICULTURAL SECTOR DEVELOPMENTSUPPORT PROGRAMME (ASDSP)

REPUBLIC OF KENYA


2014

Agricultural Sector Development Support Programme (ASDSP)

Ministry of Agriculture, Livestock and FisheriesHill Plaza, 6th Floor, P.O. Box 30028-00100 Nairobi

Tel/Fax: +254-20-2714867Email: [email protected]

www.asdsp.co.ke

NAIROBI COUNTY

Volume 1: HOUSEHOLD BASELINE SURVEY REPORT


household baseline survey report - … secretary ministry of agriculture, livestock and fisheries....

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