Siaya County

Prioritized Value Chains Suitability Atlas

October, 2020

i Indigenous chicken, mango

and fish FOREWORD

A thriving commercialized agricultural sector is critical to the realization of ’s blue print vision 2030. Agricultural sector is the backbone of the economy with a great potential for growth and transformation. The Kenyan economy is substantially driven by a thriving agriculture sector that is contributing 33% of total GDP, 60% of informal employment and 60% of exports. Through public and private sector involvement, progress is being made in the modernization of agricultural sector through market led approaches. Early signs of progress include productivity gains, yield increases, rising incomes, declining poverty among others.

The Agricultural Sector Growth and Transformation Strategy (ASGTS) 2019-2029 provide a road map for the transformation. It identifies three anchors that are pivotal in the attainment of vision 2030, the Malabo Declaration under the Comprehensive Africa Agriculture Development Programme (CAADP), and the United Nations Sustainable Development Goals (SDGs). The strategic pillars target to increase small-scale farmer, pastoralist and fisher folk incomes, Increase agricultural output and value addition and address household food resilience

Siaya County with its vast agricultural resources is uniquely placed to harness the existing natural, physical, financial, social and human capital to bring about the desired change. In pursuance of the agricultural transformation strategy, foundational initiatives that twin food security and commercialization agenda are in place courtesy of the county government of Siaya and other sector players. For instance, a robust farm input subsidy and mechanization program is being implemented in the county to enhance food and nutrition security.

Agriculture Sector Development Support Program (ASDSP II) I is building on this foundation to transform and develop the value chains of fish, mango and chicken into commercial enterprises for improved incomes, food and nutrition security. Its approach is fourfold; increasing productivity of priority value chains while enabling private sector investment; strengthening entrepreneurial skills of Value Chain Actors (VCAs); improving access to markets by VCAs and strengthening structures and capacities for coordination.

The suitability mapping is supposed to enhance value chain production for economic development. Focus was on the most productive and suitable value chains for maximum exploitation. This process was highly participatory and involved stakeholders drawn from the

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Ministry of Agriculture – ASDSP and the county government’s department of Land physical and land use planning directorates. The maps so prepared are to guide in the promotion of the value chains and are to be incorporated in the physical and land use planning processes. Apart from this, the maps will be handy in the formulation of regulations and guidelines to safeguard on the value chains.

The aim of this value chain suitability mapping atlas is therefore to establish the current status of the value chains with respect to their productivity, entrepreneurial skills, market access and the chain governance. The report also sheds light on the performance of the value chains based on their potential to support livelihoods using the World Bank per capita income benchmark. The Atlas also suggests possible adaptation measures that could be implemented to increase competitiveness. The measures suggested are targeted at addressing the constraints and challenges that affect value chain productivity.

I recommend this atlas to partners, value chain actors, sector extension and development agents to inform the choice placement and development of the three prioritized value chains. It is a tool that can also be replicated to inform development of other value chains in the County. I would therefore urge you to read and apply it’s useful suggestions.

Hon. Dr. Elizabeth Odhiambo, County Executive Committee Member, Agriculture, Livestock & Fisheries, Siaya County ,

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ACKNOWLEDGEMENT

The ASDSP is implemented at county level in all the 6 sub- counties through the established County Programme Secretariat (CPS). The purpose of the suitability mapping exercise was thus to classify the level of suitability of existing biophysical, social, economic and political parameters in the promotion and commercialisation of the fish, mango and indigenous value chains in Siaya County.

I take this opportunity to extend special recognition and appreciation to the following people who made invaluable contributions that led to the success and production of this Atlas; Hon. Elizabeth Odhiambo, CECM, Department of Agriculture Livestock and Fisheries, for her leadership and support that enabled the realisation of this Atlas and Mr Charles Siso, Chief Officer Department of Agriculture, livestock and Fisheries for technical guidance. I would also wish to mention Siaya CPS staff, the entire County physical planning office and all the sector directors and staff for providing technical and logistical support. Not to be left out are all the value chain actors, the service providers and NPS for providing the data and leading the development of the Atlas.

Kenneth Otieno Owuor, County Programme Coordinator, Agriculture Sector Development Support Programme II, Siaya County

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Published by the ©The Agricultural Sector Development Support Programme Offices Hill Plaza, Community Hill and County Hqs

National and Siaya County P.O. Box 30028-00100, Nairobi, Kenya PO Box P.O. Box 849-40600 Siaya, Kenya

October 2020

Text Orodi Odhiambo Johannes (Technical Assistance), Richard Ndegwa (Programme Coordinator ASDSP), Mikael Segerros (Technical Assistance), Benjamin Ndegwa (Specialist, Climate Smart Agriculture), Christian Thine Omuto (University of Nairobi), Robert C. Kandagor (University of Nairobi) and Paul Orina (KEMFRI), Maurice Ochieng’ (Geospatial, Siaya)

Editors Paul Orina (Kenya Marine and Fisheries Research Institute, KEMFRI), Orodi Odhiambo Johannes (Technical Assistance) and Paul Wandere (Dairy Training Institute)

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EXECUTIVE SUMMARY

Siaya County is one of the six counties in Nyanza region. It has a land surface area of approximately 2,530 km² and water surface area of approximately 1,005 km2. It borders Busia, , , , and Homabay Counties. It approximately lies between latitude 0º 26´South to 0º 18´ North and longitude 33º 58´ and 34º 33´East.

The county consists of six sub-counties and thirty wards; Alego Usonga, Bondo and Gem each with six wards and Rarieda, Ugenya and sub-counties with five, four and three wards respectively. Of the six sub-counties, Alego Usonga is the largest with an approximate area of 605.8 km2 while Ugunja is the smallest with an approximate area of 200.9 km2.

The County experiences a bi-modal rainfall, with long rains falling between March and June and short rains between September and December. The relief and the altitude influence its distribution and amount. Siaya County is drier in the southern part towards Bondo and Rarieda sub-counties and is wetter towards the higher altitudes in the northern part particularly Gem, Ugunja and Ugenya sub-counties. On the highlands, the rainfall ranges between 800mm – 2,000mm while lower areas receive rainfall ranging between 800 – 1,600mm. Temperatures vary with altitude rising from 21° C in the North East to about 22.50° C along the shores of Lake Victoria. In the South, it ranges from mean minimum temperature of 16.3° C to a mean maximum temperature of 29.1° C. Humidity is relatively high with mean evaporation being between 1,800mm to 2,200mm per annum. The relative humidity ranges between 73 per cent in the morning to 52 per cent in the afternoon.

The County spreads across agro-ecological zones LM1 to LM 5. According to the Kenya Soil Survey and Integrated Regional Development plan for the Lake Basin Development Authority, the lower part of the County and especially the shores of Lake Victoria can be categorized into semi-humid, semi-dry Lower Midland zones (LM4 and LM5). These zones cover the whole of Uyoma in Rarieda Sub-County and Yimbo in Bondo Sub-County. The lower central parts of the County, covering the whole of Sakwa and Asembo in Bondo and Rarieda Sub-counties respectively and the lower parts of Boro Division are classified as the midland zone LM3. The northern part of the County comprising Gem, Ugunja and Ugenya Sub-counties and the upper parts of Boro Division in Alego Usonga Sub-County are classified as the low-midland zones

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(LM2 and LM3). These are sub-humid and humid zones with reliable precipitation. There are also pockets of upper midland zones (about 30sq.kms) in Yala Division, Gem Sub-County with a high potential for agricultural activity.

Sustainability of the ecosystem productivity and biodiversity requires quantification of quality and quantity of natural resources and their suitability for a range of land use in the planning process of future rural, urban and industrial activities. This is an important aspect in sustainable value chain development for resilient livelihoods. A resource map gives information regarding the occurrence, distribution, access to and use of resources; topography; human settlements; and activities of a community or an area in relation to community members. It’s important because it aids in the identification of constrains, mitigations and overall prevailing opportunities.

The County prioritized value chains (PVCs) namely fish, indigenous chicken and mangos have demonstrated great economic transformation. However, climate change one of the key agriculture sector challenge is likely to undermine the PVCs potential and contribute to declining yields because of the increase in frequency and intensity of droughts and floods. Therefore, there is need to design strategic and innovative interventions to increase productivity across the different value chains nodes (VCN) for VC business resilience.

Key parameters requisite for the sustainable development of the County PVCs were identified; thereafter a comparative analysis of the County parameter status with the value chain specific parameters (biophysical, social, economic and political) and a further VC specific suitability analysis undertaken to inform investment, risk and interventions. The undertaken suitability analysis has proved that the existing parameters in the county are moderately suitable for the ASDSP I specific mango and indigenous chicken PVs and C highly suitable for cage fish aquaculture.

Generally; the political landscape is characterized by a positive will to support food security for sustainable livelihoods improvement and socio-economic development as demonstrated in the Siaya County 2020 draft County Fiscal Strategy Paper. The strategy not only aims at complimenting the National Government BIG FOUR development agenda, but also the Siaya Vision 2035 development blue print. The governor’s manifesto of 2017- 2022 prioritizes improvement of socio-economic infrastructural facilities such as road networks and markets

vii which are supportive to the sustainable development of the prioritized value chains. Additionally, the manifesto has also highly prioritized investment in the development of the fisheries and the blue economy through interventions such as strengthening of the beach management units, procurement of deep-water fishing vessels and gears and capacity building of the beach management units.

In terms of road access, the county is generally moderately to highly accessible. Based on the distance covered to the market and urban centres, most areas of Ugunja, Ugenya and Gem are moderately to highly accessible while Bondo and Rarieda sub counties are rated between marginally to moderately suitable. This is in light of locals having to sometimes cover up to 12km or more to access the urban centre services.

Siaya county soils range between clayey to very clayey in different proportions across the six sub-counties. The soils are 95% moderately suitable while the remaining 5% scattered across the county are either marginally or lowly suitable thus limiting crop commercialization.

In terms of the county priority value chains, most parts of Siaya County are moderately suitable for the mango value chain with highly suitable areas being sparsely distributed across the county. The County is largely highly suitable to moderately suitable for indigenous chicken farming. The County has a fairly large area suitable for cage culture in Lake Victoria. However, it is critical to note that majority of the current cage sites are not well selected and thus may hamper the cage potential in the County.

Despite the overall suitability classification for the three priority value chains, appropriate value chain-based adaptation measures and innovative technologies are also proposed to improve value chain competitiveness.

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TABLE OF CONTENTS

FOREWORD ...... ii ACKNOWLEDGEMENT ...... iv EXECUTIVE SUMMARY ...... vi TABLE OF CONTENTS ...... ix ABBREVIATIONS ...... xiii 1. INTRODUCTION ...... 1 1.1. National Agricultural development landscape ...... 1 1.2. Value chain development resources ...... 5 1.3. The Agricultural Sector Development Support Programme ...... 6 1.3.1 ASDSP I ...... 6 1.3.2 ASDSP II purpose ...... 8 1.4 Rationale ...... 9 1.5 Objectives ...... 12 1.6 Principles...... 12 2. METHODOLOGY ...... 13 2.1. Selection of evaluation criteria ...... 13 2.2. Data gathering and preparation ...... 14 2.3. Applying MCE and Assigning weight of factors ...... 16 2.4. Overlaying the maps layers ...... 17 3. MAPPING COUNTY RESOURCES ...... 18 3.1 Agro-Ecological Zones ...... 19 3.2 Physical and Topographic Features ...... 19 3.3 Population ...... 21 3.4 County Resources ...... 21 3.4.1 Biophysical features ...... 21 3.4.2 Siaya County Agrarian Parameter ...... 26 3.4.3 Siaya County Economic Parameters ...... 28 3.4.4. Siaya County Political Landscape ...... 29 4. PRIORITIZED VALUE CHAIN SUITABILITY MAPS ...... 30 4.1 Fish Value Chain ...... 30 4.1.1 Parameter Analysis for fish value chain ...... 30 4.1.2 Suitability classification of fish value chain ...... 30 4.1.3 Adaptation measures ...... 32 4.1.4 Technological innovations ...... 33 4.2 Indigenous Chicken Value Chain ...... 33

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4.2.1 Parameter analysis ...... 34 4.2.2 County Indigenous Suitability Map ...... 35 4.2.3 Adaptation measures ...... 36 4.2.4 Adaptation technologies and innovations ...... 36 4.3 Mango value chain ...... 37 4.3.1 Parameter Analysis for mango value chain ...... 38 4.3.2 Adaptation measures ...... 41 4.3.3 Suitability classification of mango value chain ...... 38 4.3.4 Adaptation technologies and innovations ...... 43 BIBLIOGRAPHY ...... 45

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LIST OF FIGURES

Figure 1: Kenya Rainfall distribution ...... 5 Figure 2: Kenya Temperature ...... 6 Figure 3: Kenya Population Density Map ...... 7 Figure 4: Kenya Roads and Major Towns ...... 8 Figure 5: Agro ecological zones ...... 11 Figure 6: Kenya Soil Suitability Map ...... 11 Figure 7: Kenya soil suitability classification ...... 12 Figure 8: Suitability mapping process ...... 15 Figure 9: Siaya County Administrative Boundaries Map ...... 18 Figure 10: Siaya county natural resources map ...... 20 Figure 11: County Mean Annual Temperature ...... 22 Figure 12: Siaya County Rainfall Availability ...... 23 Figure 13: Siaya County Slope Map ...... 24 Figure 14: Siaya county heat stress levels ...... 25 Figure 15: Siaya County soil resource map ...... 26 Figure 16: Siaya county road accessibility map ...... 28 Figure 17: Siaya county market accessibility map ...... 29 Figure 18: Siaya County Cage Culture Suitability Map ...... 31 Figure 19: Siaya County Heat Stress Map ...... 34 Figure 20: Siaya County Biophysical Suitability Map ...... 35 Figure 21: Siaya county indigenous chicken suitability ...... 36 Figure 22: Siaya County Mango Soil Suitability Map...... 39 Figure 23: Siaya County Mango Biophysical Suitability Map ...... 40 Figure 24: Siaya County Mango Suitability Map ...... 41 Figure 25: Soil modification for improved mango productivity ...... 42 Figure 26: Siaya County Mango Rain Water Management Map ...... 42 Figure 27: Land mechanization potential for improved mango productivity ...... 43

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LIST OF TABLES

Table 1: Priority value chains ...... 4 Table 2: Saaty rating Scale ...... 16 Table 3: Sample of Pair wise comparison matrix for the soil sub-criteria for a crop ...... 16 Table 4: Sample of Pair wise comparison matrix climate sub-criteria with respect for beef ...... 17 Table 5: Sample Pair wise comparison matrix of soil, climate and topography criteria for beef . 17 Table 6: Sample Pair wise comparison between the economic aspects ...... 17 Table 7: Pair wise comparison between the social aspects ...... 17 Table 8: Siaya County Comparative Development Indicators ...... 21 Table 9: Siaya County Population Dynamics ...... 27 Table 10: Siaya county parameter analysis for fish value chain ...... 30 Table 11: Siaya county parameter analysis for the indigenous chicken ...... 35 Table 12: Siaya county parameter analysis for mango value chain ...... 38

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ABBREVIATIONS AFC Agricultural Finance Cooperation AHP Analytical Hierarchy Process AP Agriculture Policy ASDSP II Agricultural Sector Development Support Programme II ASTER Advanced Space-borne Thermal Emission and Reflection Radiometer ASTGS Agriculture Sector Transformation and Growth Strategy ATVET Agricultural Technical Vocational Education Training CBOs Community Based Organizations CECM County Executive Committee Member CI Consistency Index CIDP County Integrated Development Plan CR Consistency Rating DEM Digital Elevation Model GDI Gender Development Index GIS Geographical Information System GIZ German Agency for International Cooperation GDEM V2 Global Digital Elevation Model Version 2 GOK Government of Kenya HDI Human Development Index IDW Inverse Distance Weighted ILRI International Livestock Research Institute IPM Inter photoreceptor Matrix KNBS Kenya National Bureau of Statistics KSS Kenya Soil Survey MFIs Micro Finance Institutions NGO Non Governmental Organization RCI Random Consistency Index RCMRD Regional Centre for Mapping of Resources for Development SACCOS Savings And Credit Cooperatives SCDIP Siaya County Development Integrtaed Plan MTP Medium Term Plan PPP Public Private Partnership PVC Prioritized Value Chain PWCM Pairwise Comparison Matrix VC Value Chain VCD Value Chain Development VCN Value Chain Node

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1. INTRODUCTION 1.1. National Agricultural development landscape Agricultural development in Kenya was founded on large-scale production as advanced by white colonial settlers in the early 1900s. The development concentrated in the central and rift valley highlands which were found to be most suitable to produce wheat, coffee, tea and dairy. During this period, structures were put in place by the colonial government and the settler farmers to support commercial production and marketing of agricultural commodities. These structures included input services and output market organizations such as the Veterinary Research Laboratories in 1910, the Kenya Farmers Association (KFA) in 1923 and the Kenya Co- operative Creameries (KCC) in 1925.

Between 1900 and 1950, the colonial administration established various Ordinances aimed at controlling land use in the country. The ordinances restricted Africans to rural areas and from occupying land that belonged to other tribes. The dual restrictive policy was marked by alienation and overcrowding of Africans in villages leading to agitation and struggle for better living conditions. In the late 1940s, due to escalation of the land use crisis and dwindling economic returns from native agricultural practices, a restructuring of African agriculture by the colonial government was made. This was aimed at supporting existing colonial production of food and raw materials for exports.

The most radical and comprehensive intervention during this period was the £5 million twenty- year Swynnerton agricultural development plan that commenced in April 1954. The main thrust of this plan was to increase household incomes through radical changes in land tenure system mainly in central Kenya. Small parcels of land were consolidated into at least 10-acre units per family. These units were registered and developed to improve productivity and household earnings from agriculture that averaged £10 to £100 in cash sales per year. This action resulted in a dramatic rise in the value of recorded output from the small-holdings from £5.2 million in 1955 to £14 million in 1964 with coffee accounting for 55 per cent of the increase. The impact of this policy action resulted in significant decrease in the proportion of small holders living below poverty from over 60% in 1953 to less than 18% in 1974 in Central Kenya. This reduction was significant when compared to near zero poverty reduction levels witnessed in other parts of the country that were not covered by the plan. The major failure of the Plan was the neglect and 1

marginalization of other areas of the country which led to imbalances in development between different regions.

After Kenya attained her independence, the agricultural industry concentrated support on smallholder farming with the goal of attaining food self-sufficiency and rural development. The policy actions at this time saw the former large-scale farms in the highlands subdivided and sold to smallholder farmers. Subdivision of large-scale farms into small scale systems compromised the commercial viability of most agricultural enterprises in the productive areas of Rift Valley and Central Kenya. Small scale agricultural production reduced productivity fourfold while rural poverty increased from the low of 18% in 1974 to 25.6% by 2006 in some of these areas.

Another policy shift that had far reaching implications to agricultural development was the Sessional paper No. 10 of 1965 on African socialism and its application to planning in Kenya. This policy ensured that the country’s wealth would remain in the productive areas, which included the former white highlands and those covered by early registration under the Swynnerton Plan. It stressed that to make the economy grow as fast as possible, development funds would be invested where it would yield the largest increase in net output. This approach clearly favoured the development of areas endowed with natural resources, good land and rainfall, transport and power facilities while areas without such facilities were neglected (Kenya, 1965).

The Sessional paper No. 1 of 1986 on Economic Management for Renewed Growth re- emphasized the place of agriculture as the leading sector in stimulating growth and job creation in the country. This sessional paper prompted the profound structural adjustment process ever initiated by the Kenya government. A key element of this policy development was the liberalization of the production and marketing of important agricultural commodities like maize.

Other efforts geared at improving agricultural production by national government aligned to land use planning before the advent of devolution included provision of targeted extension services including the Training and Visits Extension Program, The Catchment Approach to Soil Conservation and the focal area approach of the National Agriculture and Livestock Extension Program (2000). The Economic Stimulus Program (ESP) of 2009/2010 was another national government initiative that committed financial support aimed at jumpstarting the Kenyan

2 economy towards long term growth and development. Priority areas in agriculture were skewed towards construction of horticultural markets and promotion of small holder inland aquaculture. Government interventions and programs in agricultural sector during the intervening period from 1963 to 2013 were not informed by any spatial plans that linked the resource base to agricultural development.

Following the promulgation of Kenya Constitution 2010, the country transited into a devolved government system in 2013 with agriculture becoming a devolved county function. The Kenya 2010 Constitution ushered a new planning system with the national and county governments tasked to develop national and county specific spatial maps to support zoning and designation of areas for production of scheduled agricultural commodities. The Kenya National Spatial Plan 2015-2045: An integrated Spatial Plan for Balanced and Sustainable National Development, was developed within this constitutional framework and has laid the foundation on which Article 66, on the regulation of land uses, Article 68, on maximum and minimum land holding sizes and Article 69 on environment management will be achieved. The Kenya Crops ACT 2013 designates the Cabinet Secretary in charge of Agriculture with the advice of the Agricultural and Food Authority with the responsibility of developing rules for identifying and zoning agricultural land suitable to produce the scheduled crops. The Crops ACT 2013 however allows individual landowners to have a final say on the actual land use practice to implement.

The suitability maps developed are meant to inform competitive land use practices to support promotion of priority value chains in the 47 . The Atlas produced builds on the demands for spatial planning and regulation of land uses by examining the suitability of the Kenyan land resource in supporting some 29 priority value chains (PVC). The maps offer an interim evaluation of and demonstrate to some extent the underlying reasons behind the decline in agricultural productivity. They pick out the potentialities that exist in support of commercialisation of the 29 priority value chains (Table 1). The value chain suitability maps provided here are aligned to value chain commodities promoted under the Agriculture Sector Development Support Program (ASDSPII).

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Table 1: Priority value chains County Prioritized Value Chain County Prioritized Value Chain 1 Baringo Meat Goat, Honey, Cow Milk 25 Meat Goat, Camel Milk, Kales 2 Cow Milk, Maize, Irish Potato, 26 Meru Indigenous Chicken, Maize and Indigenous Chicken Cow Milk 3 Cow Milk, Indigenous Chicken, 27 Migori Cow Milk, Sweet Potato and Tomatoes Indigenous Chicken 4 Busia Indigenous Chicken, Ground Nut, 28 Fish, Local Vegetables and Cow Fish Milk 5 Elgeyo Cow Milk, Irish Potato and Maize 29 Murang’a Banana, French Beans and Cow Marakwet Milk 6 Embu Cow Milk, Banana and Indigenous 30 Nairobi Broilers, Kales and Cow Milk Chicken 7 Tomatoes, Camel Milk and Beef 31 Pyrethrum, Fish and Cow Milk 8 Indigenous Chicken, Fish and 32 Nandi Maize, Indigenous Chicken and Sorghum Fish 9 Beef, Camel Milk, Tomatoes 33 Maize, Beef and Cow Milk 10 Cow Milk, Tomatoes and Beef 34 Irish Potato, Fish and Cow Milk 11 Kakamega Cow Milk, Maize and Indigenous 35 Banana, Local Vegetables and Chicken Cow Milk 12 Cow Milk, Tomatoes and 36 Irish Potato, Indigenous Chicken Indigenous Chicken and Beef 13 Cow Milk, Indigenous Chicken 37 Samburu Maize, Honey and Indigenous and Banana Chicken 14 Cassava, African Eye Bird Chilli 38 Siaya Mango, Fish and Cow Milk and Indigenous Chicken 15 Kirinyaga Cow Milk, Banana and Rice 39 Taita Banana, Indigenous Chicken and Taveta Mango 16 Kisii Cow Milk, Banana and Indigenous 40 Tana River Beef, Fish and Mango Chicken 17 Kisumu Indigenous Chicken, Fish and 41 Tharaka Cow Milk, Indigenous Chicken Cotton Nithi and Banana 18 Indigenous Chicken, Gadam 42 Trans Nzoia Maize, Indigenous Chicken, Fish Sorghum and Green Gram 19 Indigenous Chicken, African Eye 43 Turkana Sorghum, Meat Goat and Fish Bird Chilli and Passion Fruit 20 Laikipia Maize, Cow Milk and Sheep and 44 Uasin Gishu Passion Fruit, Indigenous Goats Chicken and Cow Milk 21 Indigenous Chicken, Fish and 45 Vihiga Indigenous Chicken, Cow Milk Cashew Nut and Banana 22 Cow Milk, Indigenous Chicken 46 Water Melon, Indigenous and Mango Chicken and Camel Milk 23 Makueni Indigenous Chicken, Mango and 47 West Pokot Honey, Indigenous Chicken and Green Gram Meat Goat 24 Tomatoes, Camel Milk and Meat Goat

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This is a five-year program (2018-2022) of the Ministry of Agriculture, Livestock, Fisheries and Cooperatives. It is funded by the National and the County Governments of Kenya, The Swedish government and the European Union. In , the priority value chains are cow milk, maize, indigenous chicken and Irish potato (Table 1).

1.2. Value chain development resources The Kenyan agricultural development is mainly land and climate depended. The constitution of Kenya 2010 under Article 260 defines land broadly to mean the surface of the earth and the subsurface rock; any body of water on or under the surface; marine waters in the territorial sea and exclusive economic zone; natural resources completely contained on or under the surface; and the air space above the surface. The constitution under Article 60 calls for efficient, productive and sustainable use of land. Kenya is a diverse country with rainfall and temperature endowments that support a wide scope of crop, livestock and aquaculture systems. The country receives between 250mm to over 2000 mm of rainfall (Figure 1) with temperature ranges as low as 4.6° C and highs of over 34° C (Figure 2).

Figure 1: Kenya Rainfall distribution

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Figure 2: Kenya Temperature The demand and distribution of agricultural produce within the country is affected by population density (Figure 3), purchasing power and infrastructure development (Figure 4). These attributes are key proxies to determining internal market access and size. The Kenyan population is not uniformly distributed across and within the counties meaning that demand for commodities is also not uniform. On the other hand, over the years the government has invested in the development and expansion of the road and railway networks. These actions have contributed to improving market access for both the inputs and agricultural commodities.

1.3. The Agricultural Sector Development Support Programme 1.3.1 ASDSP I Agriculture Sector Development Support Programme (ASDSP I) was a national formulated and implemented programme financed by The Government of Kenya and The Government of Sweden. The first phase was implemented during a period of five years (2012-2017).

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Figure 3: Kenya Population Density Map The developmental objective (purpose) of ASDSP I was “increased and equitable incomes, employment and improved food security of the target groups as a result of improved production and productivity in the rural smallholder farm and off-farm sector”. It was one of the major programmes implementing the sector strategy, Agriculture Sector Development Strategy (ASDS: 2010-2020) whose goal was to commercialize agriculture. During this programme phase, each county prioritized three agricultural value chains for promotion.

The priority value chains (Table 1) were identified through a scooping and consultative study forum facilitated by a team of experts in each of the seven regions of the country (the then Provinces except Nairobi, which was paired with Central). The 10 point criteria developed to guide the stakeholders in identifying and prioritizing the value chains examined among others; potential to increase in productivity; potential for private sector participation and crowding in; potential for contribution to sustainable land and natural resource management (NRM); competitiveness of the sector; unmet market demand; market size and growth prospects; profitability of enterprise; potential to contribute towards food security; potential to generate

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employment; potential for value addition; potential for women and youth involvement; potential for participation of vulnerable groups (i.e. low investments/quick returns enterprises) and Cultural Acceptability. Application of these criteria led to the selection of 29 priority value chains (PVCs) three in each of the 47 counties with the most preferred value chains being dairy, indigenous chicken, maize and fish (Table 1).

Figure 4: Kenya Roads and Major Towns 1.3.2 ASDSP II purpose The overall goal of ASDSP II is aligned to the Agricultural Policy and is to contribute to “Transformation of crops, livestock and fisheries production into commercially oriented enterprises that ensures sustainable food and nutrition security”. ASDSP II purpose is to commercialize priority VCs with expectations of increasing incomes among the VCAs and assure attainment of food and nutrition security to the VCAs households. The programme is devolved to all the 47 Counties. The identified outcome areas of the programme are; increasing productivity of priority value chains, enhancing entrepreneurship of priority Value Chain Actors,

8 improving access to market by VCAs and support to strengthen structures and capacities for consultation, cooperation and coordination (3Cs) in the sector.

During the roll out of ASDSP II, a simpler and easy 5-point criteria (Income, Food security, Employment creation, Environmental Sustainability and Opportunity to promote social inclusion) was applied to validate the existing PVCs (Table1) and in almost all the counties, the same PVCs promoted under ASDSP I were retained. Some counties however added an extra PVC and went ahead to invest additional resources on the programme.

1.4 Rationale The Kenya Vision 2030 aims at developing “an innovative, commercially-oriented and modern Agriculture”. This Vision is embedded in the Agricultural Policy (2016) and informs the Agriculture Sector Transformation and Growth Strategy (ASTGS: 2019-2029). Three flagship areas of the ASTGS of relevance are those that aim at;

i. Instituting measures to aid increasing household incomes beyond the poverty mark for some 3 million small scale producers,

ii. strengthening and launching priority digital and data use cases to drive decision making and performance management of the sector

iii. Establishment of systems for active monitoring of sustainable and climate-smart natural resource management of water basins, soil quality and land use.

The preparation of priority value chain suitability maps was made in response to these policy directives. The maps are meant to inform development actions of priority value chains in the county. The suitability classes provide a spatial framework for designating areas and regions for the promotion of value chains based on their comparative advantage to improve their competitiveness. Correctly aligned value chains and focused resource allocation would contribute to the attainment of agricultural transformation and growth. Transformation and growth of the agricultural sector will only be achieved when the problems and challenges of rapid and unregulated urbanization is addressed. Unplanned urbanisation leads to conversion of rich agricultural land to urban use; environmental degradation, unbalanced development of high potential areas at the expense of other areas, poor economic performance of agriculture and sub- optimal use of land and the rich natural resource endowment. The priority value chains

9 suitability maps provide a framework for addressing challenges by providing strategies to address the challenges based on land capability classes.

The ASDSPII outcome area one seeks to increase productivity of the priority value chains through enhanced application of climate smart agricultural interventions, practices and technologies. Suitability maps are therefore an important decision tool that can be applied to demonstrate the feasible baseline productivity of geographical regions (county, ward, country etc.) and guide in generating adaptive actions to counter the excesses of climate change and unsuitable conditions. Identification and application of climate smart technologies to meet the production needs of value chain systems will facilitate commercialization.

The priority value chains suitability maps considered biophysical, economic (population, towns, road access), social (agrarian orientation and entrepreneurial disposition) and political (existence or lack of framework conditions) attributes as they affect productivity and commercialisation of the value chains. This is a departure from the conventional agro ecological zoning procedures (Figure 5) that focused on the natural resources with particular interest on soils, rainfall, altitude and temperature. It is also a departure from the soil suitability and land capability mapping (Figure 6 and Figure 7) processes that focused on a few soil parameters

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Figure 5: Agro ecological zones

Figure 6: Kenya Soil Suitability Map

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Figure 7: Kenya soil suitability classification 1.5 Objectives The objectives of the priority value chain suitability atlas are: i. To create a spatial planning context to strengthen priority value chain competitiveness; ii. To optimize allocation and utilization of land, natural, human and capital resources to increase value chain productivity and competitiveness; iii. To secure the natural environment for high quality of life; 1.6 Principles The principles that guided the preparation of priority value chains suitability maps are; i. Transformation and commercialisation of agricultural value chains. That the value chain development must be anchored on scales that are commercially viable and technically feasible with direct benefits accruing to VCAs in incomes and food security terms. The maps were prepared to address the needs to prudently allocate resource to drive commercialisation and transformation of agriculture

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ii. Consultation and effective public and cross sectoral participation and engagement: All the maps were prepared in a participatory and consultative manner with relevant stakeholders and sectoral actors. The process involved experts from Survey of Kenya, county physical planners, Kenya Agricultural Research Organisation, Kenya Marine and Fisheries Research Institute, State and County agricultural personnel, value chain actors, universities and the private sector. iii. Value chain approach to agricultural and rural development: Development of the maps considered factors that affect production, trade and marketing to derive parameters that most represent the ease of commercialising a value chain represented as suitability classes. iv. Knowledge driven and evidence-based planning and development: The process was driven by application of scientifically proven processes and tools to capture, query, analyse data, synthesize information for presentation and use by stakeholders. v. Climate smart agriculture and green growth: The maps and the notes present measures that promote sustainable use of natural resources, increase resilience to climate change effects while leaving low carbon footprints.

2. METHODOLOGY

The suitability maps were generated through integration of a set of parameters that were derived through expert opinion and literature review. The criteria considered were grouped into four main categories namely; biophysical (land, water, climate), economic (population density, proximity to roads and markets and poverty index), social (agrarian orientation) and political (policies and supportive framework conditions). The parameters were processed as thematic maps and consolidated by overlaying to produce suitability classes of land use practices on a GIS environment using QGIS, ILWIS, SAGA and R Studio. This approach was a progression from the traditional land suitability and land evaluation mapping process.

2.1. Selection of evaluation criteria The biophysical parameters were assessed on the basis of climatic (rainfall, temperature, humidity and temperature humidity index) and soil (soil pH, soil CEC, soil organic carbon, soil texture, soil drainage, soil depth, available soil water and soil fertility, topography, length of

13 growing period, stoniness and proximity to water resources) criteria. The economic criteria were based on total population, population density, proximity to roads/rail, and proximity to marketing points. The proxy indices were applied as representations for establishing market demand and access. The agrarian culture of the people was a proxy for examining the potential growth and adoption of a value chain. These parameters were used to determine suitable areas for promoting any crop, livestock or fish value chain through a methodological process as illustrated below (Figure 8).

An Analytical Hierarchical Process (AHP) as a Multi Criteria Evaluation was used to determine relative importance of each criterion and the resulting weights were used to construct the attribute maps/layers on the GIS platform. It was preferred because of its capacity to integrate a large quantity of the heterogeneous data. A further processing of the attribute maps was done overlaying them to generate suitability composite maps. The composite maps were then subjected to a validation process from where the explanatory notes were made and incorporated in this atlas

2.2. Data gathering and preparation Soil data was obtained from Kenya Soil Survey (KSS) Land Information Cradle (online) and from the ILRI GIS (online). Climate data was obtained from Kenya Meteorological Services (KMS – online services). The socio-economic data was obtained from Kenya National Bureau of Statistics (KNBS). The huge climate data from the KMS were interpolated to get the climate information of all the 47 Counties. Satellite image and Digital Elevation Model (DEM) were obtained from Regional Centre for Mapping of Resources for Development (RCMRD) at 30- meter spatial resolution and re-projected to WGS84 coordinate system. The slope information was obtained from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2) and processed on ILWIS and SAGA to analyse the terrain.

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Figure 8: Suitability mapping process

Thematic maps for the slope and the soil parameters were developed using QGIS 3.4.2 software. Annual rainfall and mean annual temperature thematic maps were generated using Inverse Distance Weighted (IDW) interpolation. IDW interpolation determines cell values using a linearly weighted combination of a set of sample points. All the maps were geo-referenced to WGS84 coordinate system. Suitability levels Highly Suitable S1, Moderately Suitable S2, Marginally Suitable S3 and Not Suitable N were assigned scores 1, 2, 3, and 4 respectively. Pairwise ranking and weighting was done to the sub-criteria and classes with higher scores subjected to suitability evaluation. The thematic maps were resampled and reclassified before being run on the SAGA and ILWIS for the final output.

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2.3. Applying MCE and Assigning weight of factors To determine relative importance/weight of criteria and sub criteria, AHP method of MCE was used. In order to compute the weights for the four (4) criteria (biological, physical, social and economic aspects) and the sub-criteria (Soil pH, Soil Texture, Soil Depth, Soil Drainage, Soil Fertility, Soil OC, Soil CEC, Stoniness, Soil AWC, Slope, Rainfall, Temperature, Relative Humidity, Length of Growing Period, Market Proximity, Road Proximity, Temperature- Humidity Index, and Agrarian Culture), a pairwise comparison matrix (PWCM) was constructed using information obtained from Agricultural Sector Development Support Programme (ASDSP) experts gathered at the Morendat Training Centre, in June/July 2019 during an ASDSP sponsored validation workshop. During this exercise, each factor was compared with the other factors, relative to its importance; on a scale of 1 to 9 based on Saaty rating scale (Table 2). The experts provided direction on county specific interrelationships between the parameters as they affect productivity and commercialisation as illustrated in Tables 3 to Table 7. During the pairwise ranking, inconsistencies were checked by ensuring that the corresponding consistency ratio (CR) was less than 10% (Triantaphyllou et al, 1995). The CR was obtained by working with the Consistency Index (CI) and the Random Consistency Index (RCI).

Table 2: Saaty rating Scale Intensity Definition Explanation 1 Equal importance Two factors contribute equally to the objective. 3 Somewhat more important Experience and judgement slightly favour one over the other. 5 Much more important Experience and judgement strongly favour one over the other. 7 Very much more important Experience and judgement very strongly favour one over the other. Its importance is demonstrated in practice. 9 Absolutely more important The evidence favouring one over the other is of the highest possible validity. 2,4,6,8 Intermediate values When compromise is needed

Table 3: Sample of Pair wise comparison matrix for the soil sub-criteria for a crop pH Texture Depth Drainage Fertility OC CEC Stoniness pH 1 1/3 1/3 1/3 5 7 1/4 3 Texture 3 1 3 3 1/7 1/3 1/3 3 Depth 3 1/3 1 1/2 3 5 6 1/3 Drainage 3 1 /3 2 1 5 9 7 5 Fertility 1/5 7 1/3 1/5 1 1/3 1/3 5 OC 1/7 3 1/5 1/9 3 1 4 5 CEC 4 3 1/6 1/7 3 1/4 1 6 Stoniness 1/3 1/3 3 1/5 1/5 1/5 1/6 1

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Table 4: Sample of Pair wise comparison matrix climate sub-criteria with respect for beef Temperature Rainfall Temperature 1 1/3 Rainfall 3 1

Table 5: Sample Pair wise comparison matrix of soil, climate and topography criteria for beef Parameters Soil Climate Topography (slope) Soil (Biological) 1 3 7 Climate (Physical) 1/3 1 5 Topography (slope) 1/7 1/5 1

Table 6: Sample Pair wise comparison between the economic aspects Parameter Road proximity Market proximity Total population Road proximity 1 4 5 Market proximity 1/4 1 6 Total population 1/5 1/6 1

Table 7: Pair wise comparison between the social aspects Population density Agrarian culture Population density 1 3 Agrarian culture 1/3 1

2.4. Overlaying the maps layers The reclassified thematic maps/layers of each variable were weighted using the weights derived from the AHP process and the Boolean algebraic logic. The weighted maps/layers were combined by performing the weighted overlay using SAGA, Raster calculator and ILWIS to produce the final suitability map.

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3. MAPPING COUNTY RESOURCES Siaya County is one of the six counties in the Nyanza region. The land surface area of Siaya County is 2,530km² and the water surface area is 1,005 km2. It borders to the North West, Vihiga and Kakamega counties to the North East, to the South East and Homabay County across the Winam Gulf to the South. The water surface area forms part of Lake Victoria (the third largest fresh water lake in the world). The County lies between latitude 0º 26´ South to 0º 18´ North and longitude 33º 58´ and 34º 33´ East. The county consists of six sub-counties and thirty wards (Figure 9). Alego Usonga, Bondo and Gem sub counties have six wards each; Rarieda, Ugenya and Ugunja sub counties have five, four and three wards respectively. Of the six sub-counties, Alego Usonga is the largest with an approximate area of 605.8 km2 while Ugunja is the smallest with an approximate area of 200.9 km2.

Figure 9: Siaya County Administrative Boundaries Map The County experiences a bi-modal rainfall, with long rains falling between March and June and short rains between September and December. The relief and the altitude influence its distribution and amount. Siaya County is drier in the western part towards Bondo and Rarieda

18 sub-counties and is wetter towards the higher altitudes in the eastern part particularly Gem, Ugunja and Ugenya sub-counties. On the highlands, the rainfall ranges between 800mm– 2,000mm while lower areas receive rainfall ranging between 800–1,600mm.

Temperatures vary with altitude rising from 21°C in the North East to about 22.5 °C along the shores of Lake Victoria while in the South, it ranges from mean minimum temperature of 16.3 °C and mean maximum temperature of 29.1°C. Humidity is relatively high and ranges between 73 % in the morning and 52 % in the afternoon with a mean evaporation being between 1,800mm to 2,200mm per annum across the County. Climate variations are evident in all these areas due to human activity resulting in stochastic variation against statistics above.

3.1 Agro-Ecological Zones The County spreads across agro-ecological zones LM1 to LM5. According to the Kenya Soil Survey and Integrated Regional Development plan for the Lake Basin Development Authority, the lower part of the County and especially the shores of Lake Victoria can be categorized into semi-humid, semi-dry Lower Midland zones (LM4 and LM5). These zones cover the whole of Uyoma in Rarieda Sub-County and Yimbo in Bondo Sub-County. The lower central parts of the County, covering the whole of Sakwa and Asembo in Bondo and Rarieda Sub-counties respectively and the lower parts of Boro Division are classified as the midland zone LM3. The northern part of the County comprising Gem, Ugunja and Ugenya Sub-counties and the upper parts of Boro Division in Alego Usonga Sub-County are classified as the low-midland zones (LM2 and LM3). These are sub-humid and humid zones with reliable precipitation. There are also pockets of upper midland zones (about 30sq.kms) in Yala Division, Gem Sub-County with a high potential for agricultural activity.

3.2 Physical and Topographic Features Siaya County has three major geomorphologic areas namely: Dissected Uplands, Moderate Lowlands and the Yala Swamp. These have different relief, soils and land use patterns. The altitude of the County rises from 1,140m above sea level on the shores of Lake Victoria to 1,400m above sea level on the North. There are few hills found in the County namely; Mbaga, Odiado, Akara, Regea, Nyambare, Usenge, Ramogi, Rambugu, Abiero, Sirafuongo and Naya. River Nzoia and Yala traverse the County and enter Lake Victoria through the Yala Swamp. (GOK, Siaya County Development Profile, 2013).

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The county enjoys an interspersed flow of streams and rivers from the northern counties of Vihiga, Nandi and Kakamega all feeding into Lake Victoria. It has the second longest shore line on Lake Victoria Kenya side after Homabay (Figure 10, Siaya CIDP 2018-2022).

Figure 10: Siaya county natural resources map

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3.3 Population

The total population of Siaya County as at 2019 (Table 8) is 993,193 persons (KNBS 2019) comprising of 471,669 males and 521,496 females with an average population density of 393 persons/sq. Km. (2019 population estimates). This population constitutes 2.2 per cent of the national population. The age distribution of the county population is: 0-14 years (46.1 %), 15- 64 years (50.9 %), 65+ years (3.0 %) with the number of Households estimated at 199,034. This population is largely a result of high fertility, which is currently 5.5 children per woman, compared to a national average of 4.6 children per woman.

The main economic activities include subsistence farming, livestock keeping, fishing, rice farming and small-scale trading. Siaya County performs below the national average on most socio-economic indicators. The Human Development Index (HDI) is 0.46 against the national average of 0.56. Table 8: Siaya County Comparative Development Indicators Indicator SIAYA KENYA Human Development Human Development Index (HDI) 0.46 0.56 and Gender Inequality Gender Development Index (GDI) 0.42 0.49 Poverty Proportion of Population Below the 38% 45% Poverty Line Education Primary School Pupil/Teacher Ratio 42:1 52:1 Secondary School Pupil/Teacher Ratio 31:1 31:1 Source (GOK, Siaya County Development Profile, 2013)

3.4 County Resources 3.4.1 Biophysical features Siaya County Temperature Temperatures vary with altitude rising from 21° C in the North East to about 22.50° C (Fig. 11) along the shores of Lake Victoria while in the South, it ranges from mean minimum temperature of 16.3° C and mean maximum temperature of 29.1° C.

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Figure 11: County Mean Annual Temperature Siaya County Rainfall The County experiences a bi-modal rainfall, with long rains falling between March and June and short rains between September and December. The relief and the altitude influence its distribution and amount. Siaya County is drier in the southern part towards Bondo and Rarieda sub-counties and is wetter towards the higher altitudes in the northern part particularly Gem, Ugunja and Ugenya sub-counties. On the highlands, the rainfall ranges between 800mm – 2,000mm while lower areas receive rainfall ranging between 800 – 1,600mm.

According to Figure 12 below, rainfall is higher (>1600mm) in the Far East parts of the county bordering Vihiga, Kakamega and Kisumu while most parts along the lake have very low (<1000mm to 1200mm) rains.

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Figure 12: Siaya County Rainfall Availability

Siaya County Topography The County is diverse in its topographical formation rising from flat areas around the lake to steep regions on the far north. Topography is a critical factor in crop production. The slope resource map (Figure 13) informed the modification and mechanization approaches for the County’s prioritized crop value chains to ensure sustainable management of the land to sustain the commercialization processes.

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Figure 13: Siaya County Slope Map

Siaya County Heat Stress Levels The county has areas with an overall heat stress range of between 62.96 THI to 68.09 THI (Figure 14). Dairy cattle have a normal body core temperature of 38.5 to 39.3°C. The thermo neutral or comfort zone for cows is an environmental temperature range of 5 to 25 degrees Celsiusn and heat stress levels below 65.

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Figure 14: Siaya county heat stress levels

Siaya County Soil Resources Siaya county soils range between clayey to very clayey with portions of sandy and loamy soils in different proportions across the six sub-counties as indicated in Figure 15. Over 95% of the soils are moderately suitable while 5% scattered across the county are both marginally or lowly suitable thus limiting crop productivity and compromising commercialization. The key factor constraints are low organic carbon, low fertility and very low available water.

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Figure 15: Siaya County soil resource map 3.4.2 Siaya County Agrarian Parameter Siaya County has a varied population group due to its proximity to counties of different ethnicities. The Luo community is the dominant Kenyan ethnic group in the county and are mainly engaged in fishing, crop and livestock production. The other communities are Kisii, Kalenjin, Suba, and Luhya in different proportions based on the proximity to the neighbouring counties.

The county’s dependency ratio is 50:50 indicating that there are 100 dependants for every 100 working age people. According to report by Population Action International, 2014, Siaya Human Development Index is estimated to be at 0.46 against the national average of 0.56. National life expectancy is 62.2 and expected years of schooling 11.1. The ranking was in categories of Very High human development, High human development, Medium Human Development and Low human development.

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Siaya County is majorly inhabited by families that trace their land ownership mostly based on their ancestral lineage. The culture of land ownership is under threat following the emerging trend of leasing or selling land for commercial activities. This trend is likely to lead to cases of landlessness in the near future but is a positive commercialisation transformation indicator.

Land ownership in the county is largely private. The average farm size in the County varies from sub-County to sub-County, for instance the average farm size for small scale farmers in Bondo sub-County is approximately 3.0 Ha while in Alego Usonga sub-County is 1.02 Ha. The average farm size for large scale farm stands at approximately 7.0 ha.

In terms of population distribution by urban and rural residence, 89 per cent of the population in Siaya County lives in the rural areas, with only 11 per cent living in the urban areas of the county. This pattern of settlement is attributed to the nature of the county’s economy which is rural based and the inadequacy of basic infrastructural and socio-economic facilities in urban areas. This has slowed urbanization and its associated market demand for agricultural products benefits (Table 9) Table 9: Siaya County Population Dynamics County/ sub Population Land Area Population Density Households Household counties (Sq. Km) (No. per Sq. Km) size Siaya. 993,183 2,529.8 393 250,698 3.9 Siaya-Alego 224,343 598.9 375 57,553 3.9 Gem 179,792 405.3 444 44,884 4.0 Ugenya 134,354 323.5 415 33,565 4.0 Ugunja 104,241 201.0 519 26,328 3.9 Bondo 197,883 598.8 330 51,362 3.8 Rarieda 152,570 402.4 379 37,006 4.1 Source: KNBS 2019 Kenya Population and Housing Census Vol. 1

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3.4.3 Siaya County Economic Parameters The economic parameters considered include road and market access described in the sections below. Road Network and Market Access The County is experiencing a rapid growth in the opening and upgrading of the road network. This is mainly through focused interventions by both the National and County governments’. This has enhanced market access to agricultural VCAs across the County. Towns and urban centres in Siaya County are generally well served with all categories of roads. However, some of the roads within the urban centres need to be upgraded (Figure 16 and Figure17)

Figure 16: Siaya county road accessibility map In terms of road access, the county is generally moderately to highly accessible. Based on the distance covered to the market and urban centres, most areas of Ugunja, Ugenya and Gem are moderately to highly accessible while Bondo and Rarieda sub counties are rated between marginally to moderately accessible. This is in light of locals having to sometimes cover up to 12km or more to access the urban centre services (Figure 17).

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Figure 17: Siaya county market accessibility map

3.4.4. Siaya County Political Landscape The political landscape considered the extent to which the government intervened in the priority value chains in terms of providing supportive framework conditions and funding. In Siaya county, most of the policies formulated are still at draft stage awaiting public participation. These are; Agriculture draft policy, soil management draft policy, livestock draft policy, root crops draft policy. A fisheries bill is in place with an on-going development of the regulations.

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4. PRIORITIZED VALUE CHAIN SUITABILITY MAPS 4.1 Fish Value Chain

Fish has become an increasingly important part of the consumers’ diet, both directly and indirectly. It is estimated that up to 55% of national average protein intake is from fish. Not only is it important as food but it also contributes significantly to employment and income. Due to the growing global demand for the fish products, fish farming has witnessed unprecedented growth in the recent past. Tilapia fish is one of the fish species that has received significant attention. It is not only the second most important farmed fish globally, next to carps, but is also described as the most important aquaculture species of the 21st century (Shelton, 2002). Farming of fish in Siaya County has various parameters that will favour its optimal performance (Table 4.1).

4.1.1 Parameter Analysis for fish value chain Biophysical, social cultural and economic parameters were considered when developing the value chain map. The sub parameters considered included; water temperatures, soil texture, road access, market access, and slope and government political landscape (Table 10).

Table 10: Siaya county parameter analysis for fish value chain Parameters County specific Value chain Evaluation requirements requirements Water Temperature 24-27 °C <20 or>32 Highly suitable Soil texture (% clay Clayey <5 or>50 Suitable around water systems Slope (%) 3-50% <2->15 Moderately suitable Social Fisher folks Fish oriented Highly suitable Population High poverty Density Moderately suitable Roads Accessible Accessible Moderately suitable Markets- Farm gate sales Accessible Accessible Marginally suitable Access to local and Accessible <1 km-< 200km regional markets Marginally suitable Political Good will exists Good will Highly suitable

4.1.2 Suitability classification of fish value chain

Siaya County has a fairly large area suitable for cage culture in Lake Victoria. However, it is critical to note that majority of the current cage sites are not well selected and thus may hamper the cage potential in the County (Fig. 18).

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Figure 18: Siaya County Cage Culture Suitability Map

The fish value chain development was considered based on three systems; capture fisheries, inland fish aquaculture and cage fish aquaculture.

Capture Fisheries Lake Victoria forms the main natural fisheries resource in Siaya County accounting for over 95.2% of the annual county fish production. Three commercial fish species are captured, landed and traded-in the county namely, Lates niloticus (Nile perch), Rastrineobola argentae (Dagaa/ Omena) and Oreochromis niloticus (Nile tilapia). Tilapia constitutes about 4.1% of the county’s capture fish production.

Fish production in Lake Victoria is artisanal, employing mainly gill nets, baited long lines, traps and seine nets. There are currently no mechanized fishing activities in the Kenya part of Lake Victoria. Due to increased human population, there has been increased pressure on the lake resources to meet the livelihood demands. This has resulted into increased fishing pressure on the fishery with too many people chasing few fish. Fish production from L. Victoria, like other

31 natural fisheries resources world over, has therefore been declining, and so is the fish catch per unit effort (fish catch per person /per boat per day). Tilapia capture productivity stood at an average of 17 kg/boat 2018 down from an average of 42.5kg/boat/day recorded in 1981.

Aquaculture Production Aquaculture has evolved as a vehicle for addressing food insecurity given the dwindling fish stocks in the lake and at the same time enhancing livelihoods. It combines both the pond and cages. Currently Siaya County has over 720 ponds under fish farming but the number is gradually going down while in-shore cage culture is at an accelerated rise. The most popular ponds are the earthen and the dam lined. Two commercial warm water species have proven popular namely, Oreochromis niloticus (Nile tilapia) and Clarias gariepinus (African Catfish) with the former being dominant (85%) in this region (ETC East Africa, 2016)

Fish Cage culture One of the production systems that have gained traction in the recent past attracting so much investment is the Cage culture technology. Producers rear fish in confined cages in the natural lake water to meet the ever-increasing fish demand. The cages are stocked with fingerlings, fed on commercial feeds until they attain maturity weight after 6-8 months. The population of cages is estimated at 2,989 valued at Ksh. 955.4 Million (9.6 million USD). This presents lucrative business opportunities in areas such of feed production, seed fish production, cage fabrication among others. Additionally, it has created over 500 jobs directly and indirectly created income opportunities for over 4,000 people in rural and urban settings (Orina.P, 2018). This value chain can be commercialized easily due to its moderate suitability. It requires positioning of cages at recommended ideal sites, stocking density, management of seed and feed quality to be able to increase VC incomes.

4.1.3 Adaptation measures Most areas in Siaya County are moderately suitable for fish production either as capture, culture and cage fishing. To commercialize the fish value chain further, the following adaptation measures are proposed: To address challenge of porosity of soils, the challenge of seepage can be managed through the use of dam liners; where the slopes are destructive, slope reduction/modification through terracing could be applied. Where the temperatures are low temperature modulation mechanism need to be put in place; inadequate water availability in

32 areas far away from the lake shore line can be improved through rainwater harvesting. Other interventions include provision of cold storage infrastructure; support fish breeding; Intensification of the production systems; establishment of fish feeds formulation and manufacture centres; value addition. Efforts should also be geared at supporting formulation of policies and regulation and implementation of existing regulations and policies.

4.1.4 Technological innovations The following innovations are proposed: Greenhouse fish production in cooler parts of the county; Roof water catchment for fish production in the drier parts; fish feed processing units; hatcheries establishment; aggregation centres; branding of processed products and value addition

4.2 Indigenous Chicken Value Chain Indigenous chicken value chain ranks highly amongst the priority value chains in the county because of its potential to accelerate inclusive socio-economic development. It not only plays a significant role in food security for farmers and rural communities but also for urban dwellers. The urban population consumes the usual poultry cuts; the legs, heads and intestines to contribute to food security. The indigenous chicken value chain position is slowly tilting toward commercialization due to a number of factors. First, the demand for white meat is steadily rising due to increased awareness of its health benefits and improved standards of living. Secondly, there is a growing transformational mind set shift to more intensive production systems that capitalize on existing technologies such as the early maturing gene pool of improved local birds, better supply of day-old chicks, technical knowledge and skills and the improved general physical infrastructure to support the value chain.

However, attaining commercialization calls for improved productivity, further strengthening of entrepreneurial skills, better linkages and access to markets and improvement of value chain governance.

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4.2.1 Parameter analysis

The environmental temperature for optimal growth of indigenous poultry lies between 6 °C to 25°C. Siaya County temperature ranges from the low of 16.3° C to a high of 29.1° C with a temperature heat index (THI) of <62% - 68% (Figure 19). Heat stress can affect the productivity of indigenous chicken and may reduce production by 50% when the conditions are not controlled. The optimum production occurs at THI of <65%. Indigenous chicken requires a temperature of <25oC. Considering these parameters, the county can be classified as having some areas that are moderately suitable and others as highly suitable (Table 11, Figure 20). The rainfall range of <800 - >1500 mm (Figure 12) and the many surface water resources provide water that can be harvested for chicken rearing.

Figure 19: Siaya County Heat Stress Map

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Table 11: Siaya county parameter analysis for the indigenous chicken Parameters County specific Value chain Evaluation requirements requirements Average Temperature 21.7C 25-30C Moderately suitable Average Humidity 52-73% <70%->80% Moderately suitable Land slope °C/% 3-50% <1 (0.3%)-25 (>7%) Highly suitable Biophysical Marginally suitable Agrarian culture >4.2-<1.5 Highly suitable

Adaptation Innovations Figure 20: Siaya County Biophysical Suitability Map

4.2.2 County Indigenous Suitability Map Most parts of Siaya County are highly suitable to moderately suitable for commercialisation of indigenous chicken. Because of the relatively high relative humidity in the lower sub-counties of Rarieda Bondo, and parts of Alego Usonga, the areas tend to be moderately to marginally suitable for indigenous chicken (Figure 21).

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Figure 21: Siaya county indigenous chicken suitability

4.2.3 Adaptation measures

To enhance the resource capability and improve suitability classification in support of indigenous chicken value chain in Siaya County, various adaptation measures are required. The recommended measures include rainwater management for improved soil moisture availability to enhance the yields of cereals used for feeding indigenous chicken, water harvesting for drinking and for processing poultry products and agroforestry to modify heat stress levels. The adaptation measures should aim to address proper feeding, good housing, general poultry health and breeding.

4.2.4 Adaptation technologies and innovations The following innovations are proposed: development of feed formulation apps, investment in solar energy sources, packaging and presentation of the chicken products, establishment of roasted and boiled eateries (choma zones), hygienic slaughtering of chicken, soil fertility

36 improvement technologies for improved production of raw materials for poultry feeds, day old chick production and feed formulation and production.

Other recommended technological innovations include; PPP initiatives for operationalization of warehousing and storage and provision of raw materials for sustainable management of the businesses; incubation initiative - hatching and brooding services; Use of feed formulation apps; promoting vaccination teams; relay production systems; automation of production systems ( drinkers, feeders, egg collection); rollout / institute chicken clubs in schools (Kuku Masomo initiative); organize for periodic chicken auctions; brand development for poultry processed products; satellite market programme; integrated Information management system for live birds poultry products; alternative feed sources ( cricket rearing, hydroponics); development and domestication of supportive policies and strategies.

4.3 Mango value chain

Mango value chain is one of the resilient sub sectors in the county. Previously a neglected crop, there is increased interest in the crop due to the potential it holds in transforming the economic livelihoods of the residents in the county. The mango value chain in Siaya County has expanded over recent years, not only in size but also in the geographical location of commercial and homestead plantings. The chain encompasses the full range of activities and services required to bring the mango product or service from its production to sale in the market. Depending on the VC functions, the actors are diverse and they include Input Suppliers, producers, middlemen, traders, transporters, wholesalers, retailers, processors and consumers. They are supported by a range of technical, business and financial service providers.

The performance of the nodes towards commercialization is varied and so is the degree of inclusivity. Women and youth participate in many activities along the mango value chain with differential impacts. A rapid evaluation of the chain reveals that whereas production of mango is overwhelmingly male dominated, the youths have found a niche in seedling production. The difference is due to factors that are linked to land ownership and its access thereof. Youths own almost 80% of existing nurseries and thus production of mango seedlings is a preserve of youths. Somewhere in marketing are women who do retailing. Here women control about 60% of the business functions, men 40% and youth 20%. More

37 efforts should target women and youth empowerment especially in harnessing opportunities that would propel them to earn more income and accumulate more assets.

4.3.1 Parameter Analysis for mango value chain

Mango requires mean temperature range of 28°C – 32°C during the growing period and mean temperatures between 10°C – 15°C before flowering. It also requires 890 – 1015 mm rainfall and a length of growing period (LGP) of >180 days. The crop requires a PH range of 5.5 – 7.5, soil drainage percentage of 1-4, OC amount ranging from 10. 214 – 14.47 and organic carbon requirement of <15 (Table 12). Considering the county specific parameters, the county biophysical parameters are classified as moderately suitable to highly suitable with some isolated areas in Alego that are marginally suitable (Table 12).

Table 12: Siaya county parameter analysis for mango value chain Parameters County specific Value chain Evaluation requirements requirements Average Temperature 18-30 °C 21-22 °C Moderately suitable Rainfall 1170-1450mm 1000-1600mm Highly suitable pH 5.5-7.5 5.5-9 Highly suitable Soil drainage moderate suitable Effective soil depth Less than 75 More than 75 and Highly marginal less than 200

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4.3.2 Suitability classification of mango value chain

The biophysical parameters, rainfall, temperature and soils are moderately to highly suitable with the spatial spread as presented in Figure 22 and Figure 23. The areas with poor drainage, low organic carbon and shallow depths are marginally to moderately suitable and require modifications to improve their suitability (Figure 23).

When the factor of markets, roads, politics and social orientation are computed, the mango value chain scores well across the county. Significant portions are moderately suitable with highly suitable areas found sparsely as shown in Figure 22.

Figure 22: Siaya County Mango Soil Suitability Map

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Figure 23: Siaya County Mango Biophysical Suitability Map

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Figure 24: Siaya County Mango Suitability Map

4.3.3 Adaptation measures

The adaptation measures necessary include land slope modification (Figure 25) through contour farming for the gentle slope (3-3.5 %), cover cropping for 3.5-5% slope, terracing for 5-8 % slope and bench terracing for over 50% slope. The spatial extent for land modification to improve productivity of the mango crop is presented in (Figure 25). Other interventions include promotion of rainwater water harvesting and conservation agriculture practices (Figure 26). The soil suitability constraints could be addressed by promoting efficient water utilization through drip irrigation, soil fertility management through promotion of composting and application of compost and championing correct use of suitable inorganic and organic fertilizers. The water management technologies proposed should address soil moisture availability. In-situ rainwater harvesting and appropriate conservation agriculture approaches are recommended (Figure 26).

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Figure 25: Soil modification for improved mango productivity

Figure 26: Siaya County Mango Rain Water Management Map

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In order to commercialise mango production, the scale of operations should be increased and this may require mechanised weeding, spraying, harvesting and on farm transport. To this end the terrain allows for mechanised systems as shown in 27. There is need for establishing functional and effective market structures and domestication and formulation of policies and regulation that stimulate growth of the mango value chain.

Figure 27: Land mechanization potential for improved mango productivity

4.3.4 Adaptation technologies and innovations

The following innovations are proposed: Solar driers and freezers, packaging and branding of the mango products- Pakacha” packaging; drip irrigation; in-situ rain water harvesting, top working; use of pheromone traps; CA, composting, soil testing and long-time development of cultivars that may withstand high temperatures.

Other technological innovations include; development of water pans to support seedling production, incubation of value chain actors especially on spraying, harvesting and seedling production, curriculum development for technical and soft skills training in Agricultural

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Technical Vocational Education Training (ATVET) institutions ; PPP initiatives for operationalization and sustainable management of existing processing plants and marketing outlets; change of production system; IPM (Biological control of mango fruit flies) e.g. pheromone traps; promotion of MAFAN clubs; development of different products (Mango sweets and flakes); establishment of aggregation centres for mango fruits; establishment of mango e-market plat forms; contractual Marketing with mango producers; branding of mango facilities and outlets and nurturing growth of SMEs.

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BIBLIOGRAPHY

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