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OPPORTUNITIES FOR SCALING UP CLIMATE SMART DAIRY PRODUCTION IN

ZIWAY-HAWASSA MILK SHED, ETHIOPIA

Sara Hailemariam

Van Hall Larenstein, University of Applied Science The Netherlands

September 2018

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OPPORTUNITIES FOR SCALING UP CLIMATE SMART DAIRY PRODUCTION IN ZIWAY-HAWASSA MILK SHED, ETHIOPIA

A research project submitted to Van Hall Larenstein University of Applied Sciences in partial

fulfilment of the requirements for the MSc in Agricultural Production Chain Management -

Livestock Production Chains.

This research has been carried out as part of the project “Climate Smart Dairy in Ethiopia and Kenya” of the professorships“Dairy value chain” and “Sustainable Agribusiness in Metropolitan Areas".

Supervisor: Marco Verschuur

Examiner: Robert Baars

Sara Hailemariam

Van Hall Larenstein, University of Applied Science

The Netherlands

September 2018

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Acknowledgement and Dedication

First of all, I would like to express my earnest gratitude to the Royal Netherlands Government through (NUFFIC) for offering me the scholarship to pursue the Master in Agricultural Production Chain Management - Livestock Chains. I also want to thank the Climate Change, Agriculture and Food Security (CCAFS) and Van Hall Larenstein University for sponsoring the research fund.

Heartfelt thanks and most profound appreciation goes to Marco Verschuur (research supervisor) for his invaluable professional advice and an unreserved approach throughout the course and this thesis work. I would like to thank Dilla University for giving study leave for the M.Sc. study. The support from Shimeles Gizachew during field also remarkable and acknowledged. Special thanks also forwarded to farmers who participated in this research.

I sincerely thank Behailu Endale and Hailemeskel Zewide for their support throughout this work. I am also thankful to Meron, Nitsuh, Sewa, Habiba, Su, Biruh, Demeke and Gode for their encouragement. Last, but not least, I thank the Almighty God for his unconditional love, protection and bringing all the people above into my life.

Finally, this thesis document dedicated to best mother of the world, Tsige G/yes and Behailu Endale (Mamush) for their affection, love and dedicated partnership in the success of my life.

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Contents

Acknowledgement and Dedication ... iii

Lists of Tables ... vi List of Figure ... vi Abstract ... vii 1.INTRODUCTION ... 1 1.1 Context ... 1 1.2 Project Description ... 1

1.2.1. Problem description (Justification) ... 2

1.2.2. Problem Statement ... 3

1.2.3 Objectives ... 3

1.2.4 Research Questions... 3

2.LITERATURE REVIEW ... 4

2.1 Overview of the Dairy sector in Ethiopia ... 4

2.2 Dairy consumption in Ethiopia ... 4

2.3 Climate-Smart Agriculture ... 4

2.3.1 Background and Concept of Climate-Smart Agriculture ... 4

2.3.2 Climate-smart agriculture in Ethiopia ... 5

2.4 The role of gender in climate-smart dairy value chain ... 5

2.5 Gross Margin of milk production ... 7

2.6 Conceptual framework ... 8 3.METHODOLOGY ... 9 3.1 Area Description ... 9 3.2 Research Strategy ... 9 3.2.1 Research framework ... 9 3.2.2 Research design ... 10 3.2.3 Data collection ... 10

3.2.4 Data processing strategy ... 12

3.2.5 Data analysis ... 12

4.RESEARCH FINDINGS ... 14

4.1 Socio-economic characteristics of households ... 14

4.2 Milk Chain Map in Ziway-Hawassa milk shed ... 15

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4.3.1 Land size ... 16

4.3.2 Purpose of keeping cattle ... 16

4.3.3 Income source ... 16

4.3.4 Interest of farmers for expansion of dairy farm ... 17

4.4 Climate smart dairy farming practice ... 17

4.4.1 Herd Composition and management ... 17

4.4.2 Manure Utilization and management system ... 18

4.4.3 Feed resources and feed availability calendar ... 19

4.5 The role of gender in Dairy Production ... 21

4.5 Awareness of farmers on animal emission ... 22

4.6 Economic analysis of milk production ... 23

4.7 Climate smart selection of feed and financial cost evaluation ... 24

4.8 Opportunities and challenges of Milk production in Ziway-Hawassa milk shed ... 27

4.9 Business model canvas of the Ziway- Hawassa milk shed ... 28

4.9.1 Business model canvas for urban dairy farmers ... 28

4.9.2 Business canvas for peri-urban dairy farmers ... 29

5.DISCUSSION ... 30

5.1 Socio-economic Characteristics ... 30

5.2 The structure of milk value chain In Ziway-Hawassa milkshed ... 30

5.3 Farming system and land size ... 30

5.4 Purpose of keeping livestock and income source ... 31

5.5 Herd composition and management ... 31

5.6 Manure Utilization and management ... 31

5.7 Feed resources and seasonal feed availability calendar ... 32

5.8 The role of gender in Climate smart dairy production ... 33

5.9 Economic analysis of the farm ... 33

5.9.1 Average milking cows and milk production ... 33

5.9.2 Consumption, supply and price of milk ... 34

5.9.3 Milk sales and Gross margin ... 34

5.9.4 Cost, revenue and gross margin of milk production ... 34

5.10 Description of Business model canvass ... 35

6. CONCLUSIONS AND RECOMMENDATION ... 36

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Appendices ... 46

Lists of Tables Table 1. Age category and educational status and sex of respondents ... 14

Table 2 Family size of the urban and peri-urban production system ... 14

Table 3. Farming system in urban and peri-urban system ... 16

Table 4. Land size in an urban and peri-urban production system ... 16

Table 5. Rank showing the purpose of keeping animals ... 16

Table 6. Plan for expanding the dairy farm in an urban and peri-urban production system ... 17

Table 7.Herd structure and average herd size in urban and peri-urban dairy production system ... 18

Table 8. Manure utilisation percentage in urban and peri-urban production ... 18

Table 9. Manure utilization options in urban and peri urban production system... 19

Table 10. Manure management systems in urban and peri urban production system ... 19

Table 11.Identified feed resource in urban and Peri-urban milk production system ... 20

Table 12. Rainy season and feed scarcity calendar ... 21

Table 13. Household members undertake dairy farming practices in the urban production ... 21

Table 14. Household members undertake dairy farming practices in the peri-urban production ... 22

Table 15. Milk production and supply in urban and peri-urban farmers ... 23

Table 16.Daily milk sales in an urban and peri-urban production system ... 23

Table 17. Cost of production in urban and peri-urban production ... 24

Table 18. Average variable cost, revenue, gross margin in urban and peri-urban production ... 24

Table 19. Nutritional value, emission and cost of different feeds in urban and peri-urban milk production system ... 26

Table 21. PESTEC and SWOT analysis of milk production ... 27

Table 22. Description of the suggested business model ... 35

Table 23. Recommended advise for farmers ... 37

List of Figure

Figure 1.Ethiopian master students research team... 2

Figure 2. Ethiopian women in milk production and processing ... 6

Figure 3. Conceptual Framework of the study ... 8

Figure 4. Map of the study area ... 9

Figure 5. Research framework ... 10

Figure 6. Focus group discussion with the smallholder dairy farmers ... 11

Figure 7. Milk Value chain Map in Ziway- Hawassa milk shed ... 15

Figure 8. Income sources of urban and peri-urban dairy farmers ... 17

Figure 9 Herd type percentage in peri-urban and urban dairy production system ... 18

Figure 10. Forage production status of urban and peri-urban farmers ... 20

Figure 11. Urban Business Model Canvas ... 28

Figure 12. Peri-urban Business Model Canvas ... 29

Figure 13. Common manure management practice Left) solid storage Right) Dung cake ... 32

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vii Abstract

This study was conducted to investigate dairy farming practices and gross margin at smallholder dairy farmer level to design a business model for scaling up climate-smart dairy in Ziway-Hawassa milk shed. 80 sample dairy farmers from five districts (which then categorised as urban and peri-urban dairy farmers) were selected purposively based on their dairy farming practice and purpose of the commissioner. The data was then collected through a survey (structured questionnaire) and focus group discussion (mapping, seasonal calendar and checklist). The collected data (quantitative and qualitative) were subjected to Value chain map, SWOT/PESTEC, gross margin estimation and SPSS. The results show that the existing milk chain structure includes input suppliers, producers, collectors, processors, retailers and consumers. The primary farming system in urban and peri-urban farming was livestock and mixed production system respectively. Milk production and selling was the primary purpose for keeping livestock. The dominant manure management was solid storage and dung for fuel. The feed resources of the milk shed were categorized as concentrates, green forage and crop-residue. Urban farmers were using more high energy concentrate than peri-urban farmers. The role of gender in milk production was significant. Women in peri-urban dairy farming were dominantly engaged in milking, milk processing and selling. The farm economics shows that cows in urban farming produced 12.1 litres per day and farmers supplied an average of 39.2 litres per day. However, peri-urban farmers consumed a large volume of milk at home and supplied 20.5 litres per day. The milk gross margin also shows that urban and peri-urban farmers collected 1.93 and 0.59 ETB per litre of milk. The study identified that, use of improved crossbreed, high energy feeds, biogas and composting as a climate-smart dairy farming practices of the shed. However, limitation and gaps also observed in manure handling, herd size and financial management. Therefore, the new recommended business model be suggested for linking farmers to different partners to increase milk productivity, reduce greenhouse gas emission from feed and manure and to enhance the economic efficiency of milk production.

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1.

INTRODUCTION

1.1 Context

Ethiopia has a great opportunity for livestock production. The country has a large livestock population, favourable environment for improved cattle breeds with less disease stress and potential for dairy development (Yilma et al., 2011). The dairy sector contributes considerably to the national Gross Domestic Product (GDP), and it has a share of 12–16% in the national GDP and 40% in the agricultural GDP (Zijlstra

et al., 2015).

Ethiopia is planned to achieve middle-income status by 2025 while developing a green economy that foster development and sustainability. The Climate-Resilient Green Economy (CRGE) was initiated to protect the country from the adverse impacts of climate change by identifying environmentally sustainable economic opportunities that could accelerate the country’s development (FDRE, 2011). The key pillars of this strategy are to improve livestock productivity to ensure food security and farmer livelihood improvement while reducing the emission. Specifically, the government selects dairy sector as a priority sector which aims to increase the annual milk production rate by 15.5% during the period Growth and Transformation Plan GTP II(2015-2020) (FAO & NZAGRC, 2017).

The Smallholder farmers represent about 85% of the population and are responsible for 98% of the milk production. Women are more involved in controlling of dairy cattle and have only limited involvement in formal processing, input supply, and retail or value chain governance. The majority of milk produced is channelled through the un-organised marketing system (Tadesse et al., 2017). With consideration of dairy sector enterprise to rural livelihoods and its potential role in poverty reduction, implementing a low-emissions development strategy for the dairy sector through the adoption of performance-enhancing technologies is expected to significantly increase milk yields with net benefits in the short and medium term producers. However, remoteness of the higher number of small-scale milk producers results in reduced farm-gate prices, increased input costs and lower returns to labour and capital. These, in turn, reduces incentives to participate in economic transactions and results in subsistent rather than market-oriented production systems.

Moreover, with an economy highly dependent on agriculture, Ethiopia is likely to suffer disproportionately from the impacts of climate change. In the other side, livestock systems have been found to be responsible for the most significant global source of methane emissions resulting from ruminant digestion and poor management of the manure (FAO,2013). The study found in 2013 showed that the dairy cattle sector in Ethiopia emitted 116.3 million tonnes carbon dioxide equivalent (CO2eq.). Individually, 56% and 43% of the total GHG emissions come from the mixed crop-livestock system and the agro-pastoral systems associated with milk production (FAO & NZAGRC, 2017). Different mitigation methods to reduce emissions from this sector are possible to detect with the identification of the most significant emission sources. 1.2 Project Description

Van Hall Larenstein University of Applied Sciences had got research call from CCAFS (Research Program on Climate Change Agriculture and Food Security) for scaling up climate-smart agriculture. The research aims to describe business models of chain actors and supporters to identify opportunities for scaling up

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good climate-smart dairy practices in Ethiopia and Kenya. It is connected to “Nationally Appropriate Mitigation Actions” (NAMA) to reduce GHG emissions from dairy production. In this research project, master students (agricultural production chain management students of VHL University of Applied Sciences) are involved for Kenya and Ethiopia case. The Ethiopian team have four members, and they divide portions of the milk value chain in the milk shed of Ziway – Hawassa. The main aim of these four research topics is to design climate-smart business models for the chain actors and supporters. These four topics (portions) finally combine to give the overall picture of the milk value chain. (Figure 1).

Figure 1.Ethiopian master students research team

Source. Research team sketch, 2018

1.2.1. Problem description (Justification)

The Ziway- Hawassa milk shed has untapped opportunities to supply milk for the area (Brandsma et

al.,2013). However different technical (feed, genotype and disease) and non-technical factors (human and

livestock population) were suppressed the growth and competitiveness of the sector (Sintayehu et al., 2008). These posses a significant challenge to ensure that livestock products will be produced sustainably without adverse effects on the environment. So, for farmers to invest in climate-smart dairy businesses, there needs to be an attractive and interactive business model which can create revenue or a form of income diversification, spreading investment risks and reducing stress on a family’s disposable income. Additionally, interventions have to have the potential for improving productivity while at the same time reducing enteric CH4emissions per unit of output.

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3 1.2.2. Problem Statement

Currently, the smallholder dairy farmer, who produce 93% of the total milk production, lack an appropriate dairy business model which results in less business competitive in the market. Moreover, there is a knowledge gap in climate-smart dairy practices, and limited research has been done on scaling up CSD. In line with this, the Netherlands government (the Climate Change, Agriculture and Food Security (CCAFS), Van Hall Larenstein University and Adamitulu research centre want to identify and design dairy business models for smallholder farmers to scale up sustainable climate-smart dairy farming in the sector. 1.2.3 Objectives

• To identify best CSD practices and design business models for smallholder dairy farmers in Ziway-Hawassa milk shed.

1.2.4 Research Questions

What is the role of smallholder dairy farmers and gender in the Ziway-Hawassa milk shed? o What is the structure of the existing milk value chain in the milk shed?

o What is the current farming system applied by smallholder dairy farmers?

o What are the current Climate smart dairy (CSD) management practices applied by smallholder producers?

o What is the role of gender in CSD farming?

What is the easily adaptable and profitable business model to scale up CSD in the Ziway-Hawassa milk shed?

o What is the current business model followed by smallholder dairy farmers? o What is the gross margin of the smallholder dairy farmers?

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4 2.

LITERATURE REVIEW

2.1 Overview of the Dairy sector in Ethiopia

Ethiopia has one of the largest livestock inventories in Africa with a national herd estimated at 59.5 million cattle. Out of this total cattle population, the female cattle constitute about 55.5 per cent. Of the total female cattle population, dairy cows totalled at 7.16 million (12.03%) with the proportion of hybrid and pure exotic female cattle population is only 1.13% (CSA, 2016/17).

Depending on different classification baselines like climate, integration of crop with livestock and land holding, dairy production system are categorised in urban, Peri-urban, Commercial and rural production system (Land O'Lakes Inc, 2010). Urban dairy farmers are situated in major town and cities, and they focused milk production for sale with very little or no land resources, and by using available resources. These systems have better access to service (AI) and inputs (feed) and better market accessibility. On the other hand, peri-urban farmers are found in the nearby urban towns. Sale of fluid milk and butter are the primary production objectives. Moreover, other than dairy, animals are also maintained for draught power.

The total cow milk production at country level was estimated at 3.1 billion litres with average daily milk yield per cow of about 1.37 litres (CSA 2016/17). The majority of milking cows are indigenous breeds with low production performance. Lower milk production performance is attributed to reduced lactation length, extended calving interval, late age at first calving, poor genetic makeup (Ahemed et al.,2010) and a shortage of livestock feed both in quantity and quality, especially during the dry season (Hassen et

al.,2010).

2.2 Dairy consumption in Ethiopia

Milk and milk products form part of the diet for many Ethiopians. They consume dairy products either as fresh milk or in fermented or soured form. Ethiopia consumes dairy products in less proportion than other African countries. The national per capita consumption of milk is 19kg/year (Staal et al., 2008). Such milk supply shortage in Ethiopia is due to the absence of sustainable approach of the dairy development to improve milk production and marketing and due to the challenges of active engagement in milk value chain and market by smallholder milk producers (Eyasu et al., 2014).

These days there is a rapid population growth and urbanisation in the country. Moreover, the income of the urban dwellers is growing. These factors have played an important role to increase the demand for dairy products, especially in urban areas. The need for dairy products depends on consumer preference, consumer income, population size, the price of the product, the price of substitutes and other factors. Increasing population growth and rising real income are the major factors that are expected to increase the demand for dairy products (Smith, 2013).

2.3 Climate-Smart Agriculture

2.3.1 Background and Concept of Climate-Smart Agriculture

Climate-smart agriculture (CSA) is developed by FAO to achieve sustainable agricultural development for food security by designing technical, policy and investment conditions under climate change (FAO, 2013).

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The approach includes social, economic and environmental dimensions of sustainable development by jointly addressing the food security, ecosystems management and climate change challenges. It comprised three main pillars; sustainably increasing agricultural productivity and incomes, adapting and building resilience to climate change, reducing and removing greenhouse gases emissions, where possible. At the farm level, the approach targeted to enhance smallholder daily livelihoods and food security challenges through environmental management, appropriate approach adoption, and technologies for the production, processing and marketing of agricultural commodities. Climate-smart agriculture also demands at the national level to put important policy, technical and financial management to include climate change adaptation and mitigation into agricultural sectors and provide a basis for operationalising sustainable agrarian development under changing conditions (FAO, 2013).

2.3.2 Climate-smart agriculture in Ethiopia

Ethiopian’s economy heavily depends on agriculture. The heavy reliance on rain-fed systems has made the sector particularly vulnerable to variability in rainfall and temperature. Climate change may decrease the national gross domestic product (GDP) by 8–10% by 2050 (BFS/USAID, 2017). Also, because land has been fragmented to satisfy the needs of new generations, most smallholder farms are between 0.5 and 2 hectares in size. The small plot sizes in the country are often insufficient to enable household food security or adequate income to invest in improved farming methods (Yirgu et al., 2013).

The agricultural sector in the country is a significant contributor to national emissions, accounting for approximately 60% of total emissions. Given that Ethiopia has the largest livestock population in Africa, most of the agricultural GHG emissions emanate from livestock-related activities (methane and nitrous oxide emissions from enteric fermentation and manure left on pastures respectively), which account for almost 92% of agricultural emissions (FAO, 2016).

For this reason, Ethiopia was at the forefront of Africa’s climate policy development. The Climate Resilient Green Economy (CRGE) initiative was initiated as a critical strategy in the more comprehensive and even more ambitious Growth and Transformation Plan, GTP (MoFED, 2010). The CRGE explains the effect of climate change on the reduction of Ethiopia’s GDP growth between 0.5 and 2.5% per year unless practical steps are taken to build resilience (FDRE, 2011). Much of this impact route will be effects in the agricultural sector.

However, Ethiopia lacks the knowledge and skills about CSA and conservation agriculture in particular. At the level of establishing a more climate-sensitive agricultural sector, many challenges remain, including lack of capacity to deliver core essential development services – such as agricultural extension, environmental management and infrastructure planning (DFID, 2011). Additionally, data on CSA and conservation agriculture, in particular, are insufficient at all levels (Jirata et al., 2016). Hence, research projects on CSA should be supported. Moreover, a comprehensive capacity development approach that builds on the sound assessment of the needs of all stakeholders is required.

2.4 The role of gender in climate-smart dairy value chain

The dairy sector is essential to Ethiopia’s economy and society. Men and women are involved in dairy value chains, but in different ways, and they face different constraints. Dairy production is particularly important to women, providing them with income and a means to meet social obligations. However,

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women have limited opportunity, to participate in all parts of the dairy value chain than men. They have a limited role in the leadership and management of cooperatives and private enterprises. Women are more involved in controlling of dairy cattle and have only limited involvement in formal processing, input supply, and retail or value chain governance. Consequently, women and youth tend to benefit much less from livestock and dairy value chains than men (Campbell and Dinesh 2017).

The cultural and biological burdens of women restrict their participation in social, political and economic affairs. These influence their potential to to take part in climate change deals and policy planning. And also hinder their opportunites in mitigating, adapting and coping with the effects of climate change. (CARE/ CIEL,2015). However, women have a crucial role in adapting and mitigating climate change. So, they must be appreciated and recognised as essential change agents for climate change.

The first step in tackling climate change challenges is creating an environment that backdrop against women empowerment and decision making. Studies suggest that mitigation and reduction of greenhouse gases have a positive correlation with women involvement (Wedeman and petruney, 2017). Currently, there is an increasing awareness in Ethiopia about the importance and role of women in dairy production. Gender differences are now more often considered at all stages of development planning and management. The government must further update dairy sector opportunities for women and men and inform policies and interventions that contribute to inclusive and gender-sensitive value chain development (Campbell and Dinesh 2017).

Figure 2. Ethiopian women in milk production and processing

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7 2.5 Gross Margin of milk production

Analysis of gross marigin involved examination of the variable costs and revenue of milk sale. According to Kibiego, et al., (2015), production of goods and services by firms cannot be done when total variable cost is higher than total revenue. Kit and IRR 2008 were used to compute the gross margin of milk production.

GM = R – TVC.

Where GM = Gross Margin R = Revenue

TVC = Total Variable Cost

These means that the gross margin derived by a smallholder farm is a measure of its performance. In this study, revenue considerd the milk value produced at the farm. The milk sale from the farm computed by the as follows

R = p.q

Where R = Revenue p = Price of milk per litre q = Milk output (litres)

Assuming that the smallholder dairy producers are operating in a perfectly competitive market structure, the only option for increasing revenue is to increase milk output. The price, p, is determined by the market. Competitiveness will occur when the following condition is achieved: GM > 0.

The higher the gross margin, the higher the level of competitiveness. So, for this study, a smallholder dairy farmer is considered competitive if the gross margin of that farm is equal to or greater than zero. Hence, such a farm is not economically efficient when it has a gross margin that is negative.

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8 2.6 Conceptual framework

The study relied on a value chain concept and presented in the conceptual framework (Figure 3). The framework summarised the various concepts of milk production at the smallholder level. It shows the different components and strategies for achieving climate-smart dairy in Ziway- Hawassa milk shed. Figure 3. Conceptual Framework of the study

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9 3.

METHODOLOGY

3.1 Area Description

The study was conducted in Ziway- Hawassa milk shed of Mid-Rift Valley of Ethiopia. The altitudes of these areas range from 1500 to 2600 meter above sea level and have a semi-arid type of climate. The Mid- Rift Valley has an erratic, unreliable and low rainfall averaging between 500 and 1300 mm per annum. The rainfall is bimodal with the short rains from February to May and long rains from June to September. The dominant production system in these areas is mixed crop-livestock farming. Cattle are the most important livestock species in the areas (Sintayehu Yigrem, 2015).

Figure 4. Map of the study area

Source : http://nextbook.co/editor/ 3.2 Research Strategy

3.2.1 Research framework

The research was started with a desk study by collecting relevant secondary data. Primery information, both qualitative and quantitative data were collected through the survey, focus group discussion, observation. Figure 5 illustrates how the steps of the research followed each other.

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10 Figure 5. Research framework

Source: Author sketch (2018)

3.2.2 Research design

The research had both qualitative and quantitative approach. Five districts namely Dugda, Adamitulu, Arsinegele, Shashemene and Kofele were selected purposively based on the purpose of the commissioner and their potential in milk production. The districts then classified in two clusters (urban and peri-urban farming system) based on their distance from major towns/cities, the purpose of production, and input use like feed. Urban farmers included were from Shashemene, Arsi-negele, Ziway and Meki town and peri-urban farmers where from kofele, Adamitulu and countryside of Meki. Based on this, 16 sample household from each district and a total of 80 respondents were used purposively based on their dairy farming practice and experience.

3.2.3 Data collection

The survey, focus group discussion, and personal observation was used to collect primary information. The information was translated into Afan-Oromo language and discussed with the owners of dairy cattle. The secondary was collected from district livestock and fishery office, annual reports, published articles and journals. Personal observation was also used as a means of triangulation. A brief description of the methods as explained below.

Desk study

The research was started by conducting desk research. The literature review was the starting entry point to scan information on topics identified. Additionally, published and unpublished reports, journals, books, and articles were used as a data source for further enrichment.

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11 Focus group discussion (FGD)

Two focus group discussion (at the beginning and end of the survey) were undertaken in Shashemene and Ziway district. The first FGD were arranged with the aim of orienting the farmers about the study and to identify farmers position (urban and peri-urban farmers), farming practice, and purpose of milk production and design the structure of milk value chain. The second FGD was undertaken to present the finding of the study and to design business model together with smallholder dairy farmers. The FGD was considered 20 dairy farmers from Ziway (4 urban and four peri-urban dairy farmers) and Shashemene (6 urban and six peri-urban dairy farmers). Participant was selected with the help of district personnel. The researcher coordinated all the facilitation of the discussion.

Figure 6. Focus group discussion with the smallholder dairy farmers

Source: Author

Different tools like a checklist, seasonal calendar, ranking and mapping were used in the focus group discussion. The checklist was used to point out the necessary issues for the discussion. The seasonal calendar was also used to identify the crop growing and rainy season of the area. Additionally, mapping and ranking were done to design the structure of the dairy value chain and rank the different farming practices respectively.

Survey

A structured questionnaire (both open and closed-ended questions) were used for the study. The questions first pre-tested and necessary amendments were included for achieving validity, consistency and clarity of the items.

Observation

Keen observations were made concerning the different dairy farming practices mainly in the manure and herd management.

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12 3.2.4 Data processing strategy

The data was collected by the two members of the research team. Each member prepared their question and then combined for field data collection. Each of them interviewed 40 sample respondents individually. After fieldwork, the team combined and arranged the survey data in excel sheet for data analysis. 3.2.5 Data analysis

Data were managed in such a way that the qualitative, as well as quantitative variables, were used for analysis. The data generated from the survey (quantitative) part of the study was analysed using the Statistical Package for Social Science (SPSS) version 20 and reported using tables, graphs and charts. Business canvas model was also used to describe the current and recommended business environment of the milk producer. Moreover, PESTEC and SWOT were used to describe opportunities and challenges of milk production in the shed. Chain map was also used to design the structure of the milk chain in the shed. Economic data: economic data of the study were tabulated using excel spreadsheet according to the characterisation of the variable cost-revenue structure.

Gross margin: of milk production was tabulated by kit and IRR 2008. The study considered milk sales as revenue. The variable cost was feed, hired labour, veterinary drug and breeding cost (AI/ natural bull).

GM = R (Revenue) - VC (Variable cost)

Production cost of milk per litre = Total Variable cost of productionTotal milk production per year

Rank analysis: with an objective of preference, the index was calculated by using Ms-Excel following the index formula used by Musa et al.,2006.

Index= Rn*C1+ Rn-1*C2.... + R1*Cn/∑Rn*C1+ Rn-1*C2....+ R1*Cn

Enteric emission: was also calculated to get the emission equivalent of the different feed resources. It is calculated by the formula given by IPCC,2006.

𝐸𝐹 = [𝐺𝐸 ∗ ( Ym

100) ∗ 365

55.65 ]

Where:

• EF = emission factor, kg CH4 per head per year

• GE = gross energy intake, per head per year

• Ym = methane conversion factor, per cent of the gross energy in feed converted to methane • The factor 55.65 (MJ/kg CH4) is the energy content of methane

The total Methane emission can be computed by multiplying the number of animals in each category by the emission factor

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𝑻𝒐𝒕𝒂𝒍 𝑪𝑯𝟒𝒆𝒏𝒕𝒆𝒓𝒊𝒄= ∑ 𝑬𝒊 𝒊 Where:

• Emissions = Enteric methane emissions, Kg CH4 per year

• EF = emission factor for the defined livestock population, kg CH4 per head per year

• NT = the number of heads of cattle /category

• T = species/category of livestock

• Total CH4Enteric = total methane emissions from Enteric Fermentation, Kg CH4 per year

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14 4.

Research Findings

This section presents the finding of the survey results, focus group discussion and personal observation. The findings were related to the research question and presented in line with the conceptual framework of the study.

4.1 Socio-economic characteristics of households

This sub-section describes the finding of the survey on the socio-economic characteristics of the sampled respondents. From the overall sampled respondents, 70% of the respondents were male, and the rest were female. The average age of respondents in urban and peri-urban production was 46 and 45 years respectively. The result further shows that the majority of the household (51.25%) were found in the age category between 36-51 years (Table 1).

Table 1. Age category and educational status and sex of respondents

Variables Categories Urban Farmers Peri-urban Farmers Total (%)

N % N % Sex Male 32 63 24 83 70 Female 19 37 5 17 30 Age Category 20-35 12 23.5 6 20.7 22.5 36-50 26 51 15 51.7 51.2 51-65 9 17.6 6 20.6 18.7 66-80 4 7.8 2 6.9 7.5 Average age 46 45 Educational status Illiterate 10 19.6 2 6.9 15 Primary 9 17.6 7 24.1 20 Secondary 7 13.7 10 34.4 21.2 Junior secondary 18 35.3 5 17.2 28.8 Diploma 4 7.8 4 13.8 10 University 3 5.9 1 3.4 5

The educational level of the respondents involved in the two-milk production system was diverse from literate to illiterate (Table 1). The cross-tabulation of the result shows that the major proportion of the sample household (35.3 % in urban and 34. 4 in peri-urban) completed junior secondary and secondary school respectively. The illiterate respondents of urban and peri-urban areas constituted of only 15%. The overall average family size in Urban and peri-urban farmers were 6.43 and 8.79 persons respectively (Table 2).

Table 2 Family size of the urban and peri-urban production system

Variable Farming N Mean+ SD P-value

Family size

Urban 51 6.43±2.3 .019*

Peri-urban 29 8.79±4.8 *significant difference at p<0.05

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15 4.2 Milk Chain Map in Ziway-Hawassa milk shed

Milk value chain mapping was done by identifying and charting the current value chain as discovered during focus group discussion together with dairy farmers and the research team. The value chain map (figure 7) shows the flow of payment and product flow in the chain. The identified stages of the milk chain were input supplying, production, collection and processing, retailing and consumption. Input suppliers were supplied feed and other production input to producers. Milk producers were smallholder dairy farmers who perform functions of milk production and sale.

Figure 7. Milk Value chain Map in Ziway- Hawassa milk shed

Source: Research team sketch (2018)

4.3 Characterization of the current farming system

The relative importance of the current farming system shows a different pattern for urban and peri-urban farmers. The result shows that 72.5% of urban farmers and 72.41% of peri-urban farmers followed livestock and mixed crop-livestock production system respectively. About agricultural activity, the significant number of (80.3%) of urban farmers were producing milk as a major activity.

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16 Table 3. Farming system in urban and peri-urban system

Variable Urban

%

Peri- urban %

P- value Farming system Livestock Production 72.5 27.5

.000* Mixed production 27.4 72.4 Major agricultural activity Milk production 80.3 51.7 .011* Crop production 19.6 48.2 *significant difference at p<0.05 4.3.1 Land size

Assessment of land size shows that all sampled dairy farmers have owned land. However, the large size of land was owned by farmers who live in the peri-urban area (3.41 ha). (Table 4). From field study, it is also observed that farmers from Kofele district-owned a large plot of land than another district.

Table 4. Land size in an urban and peri-urban production system

Variable Farming N Mean+ SD P-value

Land size Urban 51 0.67±1.3 .000*

Peri-urban 29 3.41±2.8 *significant difference at p<0.05

4.3.2 Purpose of keeping cattle

Purpose rank analysis of the study shows that the primary purpose of keeping livestock was for milk production. In the peri-urban farming, cattle were also kept to maintain drought power. However, manure production and selling were the least purposes in both production system.

Table 5. Rank showing the purpose of keeping animals Purpose of

keeping cattle

Urban farming Peri-urban farming

R1 R2 R3 Index Rank R1 R2 R3 Index Rank

Milk 51 0 0 0.57 1 27 2 0 0.53 1 Insurance 0 25 6 0.21 2 1 10 3 0.16 3 Finance 0 18 7 0.16 3 2 10 1 0.17 2 Beef 0 2 5 0.03 4 0 2 3 0.04 5 Drought 0 1 1 0.01 5 0 2 8 0.08 4 Manure 0 1 1 0.01 5 0 0 2 0.01 6 Index= (Rn*C1+ Rn-1*C2.... + R1*Cn/∑Rn*C1+ Rn-1*C2....+ R1*Cn) 4.3.3 Income source

The contribution of dairy to household income was high for both production system. The result also revealed that 76% of the urban and 55% of peri-urban farmers depend on milk sales (Figure 8). However, due to the nature of their production system, live animal and crop sales were higher in a peri-urban production system.

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17

Figure 8. Income sources of urban and peri-urban dairy farmers

4.3.4 Interest of farmers for expansion of dairy farm

Farmers were asked about their interest to expand their dairy farm in the future. The result showed that 47% of urban farmers had no interest to expand the dairy farm. On the contrary, a higher proportion of peri-urban farmers had the plan to increase the size and activity of their dairy farm (Table 6). Due to land shortage and other reasons, urban farmers were preferred to keep what they have without further expansion.

Table 6. Plan for expanding the dairy farm in an urban and peri-urban production system Do you have the

plan to expand your dairy farm? Urban Peri-urban Total N % N % Yes 27 53% 28 97% 54 NO 24 47% 1 3% 25

4.4 Climate smart dairy farming practice 4.4.1 Herd Composition and management

Figure 9 shows the percentage of herd type in the milk shed. About 89% of urban dairy farmers possessed cross breed cattle and very few local cattle. On the other hand, a high percentage of cattle (43%) in peri-urban areas were local indigenous breed. The independent sample test also showed the significant difference in holding of local and cross breed cattle in the system.

Milk Sales 55% Live-animal Sales 17% Crop Sales 28%

Peri-urban income sources

Milk Sales 76% Live-animal Sales 2% Crop sales 16% other 6%

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18

Figure 9 Herd type percentage in peri-urban and urban dairy production system

Different percentage of cattle herd structure and size was recorded in the two-farming system. On average, peri-urban farmers hold a high number of milking cows (8.3) than urban dairy farmers (3.6). Moreover, the average composition of other farm animals also higher in peri-urban dairy farmers (Table7). Table 7.Herd structure and average herd size in urban and peri-urban dairy production system

Animal type Urban Peri-urban N % Averge/HH N % Averge/HH Milking cows 187 49 3.6 243 47 8.3 Ox 8 2 0.1 37 7 1.2 Bull 10 3 0.1 33 6 1.1 Heifer 93 24 1.8 101 19 3.4 Calf 87 23 1.7 106 20 3.6

4.4.2 Manure Utilization and management system

As depicted in table 8, 86.2% of peri-urban dairy farmers were using or selling cattle manure for different purposes. However, 41.2 % of the urban sampled respondents were not using the manure for any home or agricultural purposes. The independence sample t-test also showed that a higher percentage of peri-urban farmers were using the cattle manure than peri-urban farmers.

Table 8. Manure utilisation percentage in urban and peri-urban production Do you use or sell

cattle manure? Urban (%) Peri-urban (%) P-value Yes 58.8 86.2 0.0011* No 41.2 13.8 *significant difference at p<0.05

Regarding manure utilisation, a large proportion of dairy farmers utilised cattle manure for fuel in the form of dried dung cake (Table 9). Farmers especially peri-urban farmers also used the manure as crop fertiliser. However, using manure for biogas was not a common practice although, the urban farmers had better initiations towards biogas utilisation.

43%

57%

Peri-urban herd composition

Local Cross

3%

89% 8%

Urban herd composition

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19

Table 9. Manure utilization options in urban and peri urban production system Utilization

options

Urban Peri Urban

R1 R2 R3 Index Rank R1 R2 R3 Index Rank

Crop fertilizer 4 2 0.16 2 7 9 0.37 2

Biogas 5 0.15 3 0.00

Dung cake for fuel 19 4 0.63 1 16 5 0.55 1

Construction 1 0.01 5 1 1 0.03 4

Sell 2 0.06 4 2 0.06 3

Index= Rn*C1+ Rn-1*C2.... + R1*Cn/∑Rn*C1+ Rn-1*C2....+ R1*Cn

The Study shows that management of manure as solid storage and dung for fuel was a common practice in the milk shed (table 10). Peri-urban dairy farmers managed manure for burning fuel more commonly. However, urban farmers accumulated manure as solid storage for an extended period. Management of manure as composting and anaerobic digester was limited in both systems.

Table 10. Manure management systems in urban and peri urban production system

Management Urban Peri-urban

Farmers (%) Duration of storage

Farmers (%) Duration of storage

Anaerobic digester 9.8 12 0.0 0

Burned for fuel 43.1 5.9 72.4 5.9

Composting 2.0 12 3.4 12

Daily spread 0.0 0 3.4 12

Solid storage 88.2 8.3 89.7 5.8

4.4.3 Feed resources and feed availability calendar Identified feed resource in the milk shed

The primary feed resources of the study area are listed in table 11. Fifteen types of feed resources were identified in the milk shed. From urban dairy farmers, a higher percentage of the respondents were using purchased concentrates, crop residue and a small amount of green forage. High energy diets were given in urban farming than peri-urban farming.

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20

Table 11.Identified feed resource in urban and Peri-urban milk production system

Feed Category Feed Type % of urban farmers

using the feed

% of peri-urban farmers using the feed

Green forage

Green pasture

(Bermuda grass mainly) 23.5

31

Maiz green forage 25.5 27.6

Cabbage 1.9 1.9 Alfalfa 1.9 0 Crop-residue Wheat straw 82.3 72.4 Teff straw 33.3 17.2 Barley straw 19.6 48.3 Maize stover 1.9 17.2 Concentrates

Fagullo (linseed meal) 78.4 48.3

Frushka (Wheat bran) 84.3 69

Almi ration (Mixed ration) 50.9 6.8

Cottonseed meal

(cottonseed hull) 1.9 10.3

Lentil bran 0 3.4

Nug (Nug seed cake) 0 6.9

Atella 35.2 3.4

Sugar cane molasses 0 10.3

Brewery by-products 15.6 6.8

Feed production

Figure 10 shows the status of feed and forage production in the study area. The result revealed that both urban and peri-urban farmers are not in a strong position to produce animal feed. From the sampled respondents, only 3.3% of the farmers were initiated to produce improved forage.

Figure 10. Forage production status of urban and peri-urban farmers

100 96.7

YES NO

Do you produce improved forages for dairy cows (In %)?

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21 Feed availability calendar

The rainy season of the year determines the crop residue availability for dairy animals. The main rainy season of the year starts in June and ceases in October. During focus group discussion, farmers pointed out that, these months are suitable for crop cultivation.

Further, they also indicated February, March, July and August are the most feed scarce seasons for the animals. Just after these four months, the degree of feed scarcity decreases. This is due to the collection of crop aftermath and residues following crop harvest. Wheat, barley, maize and teff straw were the dominant crop-residues in the milk shed.

Table 12. Rainy season and feed scarcity calendar

Rainy season and feed scarcity calender

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rainy Season

Feed scarce Season

Crop-residue available season

4.5 The role of gender in Dairy Production

As presented in table 13, different diary activities were undertaken by different household members. In urban dairy production, the domination of men in feed selection, feed transportation and selection of cows for insemination was high. However, females also participated in different dairy farm activities. Females especially wives were involved more in milking, processing, milk selling and manure collection. Hired female labourers also majorly participated in the milking of the cows. However, forage preservation like silage making was not practised by the two-gender cluster.

Table 13. Household members undertake dairy farming practices in the urban production Activities Husband (%) Wife (%) Daughter (%) Son (%) Hired male labourer (%) Hired female labourer (%) Manure collection 45.1 49.0 23.5 39.2 0.0 0.0 Manure application 3.9 3.9 0.0 7.8 11.7 0.0 Hay making 0.0 0.0 0.0 0.0 0.0 0.0 Silage making 0.0 0.0 0.0 1.9 0.0 0.0 Feed selection 58.8 45.1 1.9 19.6 9.8 0.0

Feed transportation by cart 54.9 37.2 9.8 23.5 13.7 0.0

Selecting cows for

insemination 70.5 41.1 1.9 13.7 9.8 0.0

Milk selling 29.4 66.6 25.4 19.6 5.8 0.0

Milk processing at home 3.9 27.4 11.7 5.8 0.0 3.9

Milking 29.4 47.0 15.6 21.5 21.5 7.8

Unlike urban dairy production, females in the peri-urban area were in the lead of undertaking different activities. Manure collection from animal barns, milking, feed selection and milk processing at home were

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22

the major activities which were done by females (Table 14). However, selecting cows for insemination remains a task performed by men (husbands). Hired labours especially male majorly participated in manure collection, feeding the animals and milking of the cows.

Table 14. Household members undertake dairy farming practices in the peri-urban production Activities Husband (%) Wife (%) Daughter (%) Son (%) Hired male labourer (%) Hired female labourer (%) Manure collection 27.5 62.0 51.7 24.1 20.6 3.4 Manure application 24.1 20.6 17.2 27.5 6.9 3.4 Feeding animals 62.0 62.0 48.2 58.6 20.6 0.0 Hay making 10.3 6.9 6.9 17.2 0.0 0.0 Silage making 0.0 0.0 0.0 0.0 0.0 0.0 Feed selection 68.9 93.1 0.0 6.9 3.4 0.0 Feed transportation 48.2 37.9 3.4 13.7 10.3 0.0

Selecting cows for

insemination 79.3 27.5 6.9 17.2 0.0 0.0

Milk selling 27.5 65.5 37.9 20.6 3.4 0.0

Milk processing at

home 0.0 72.4 34.4 0.0 3.4 3.4

Milking 13.7 72.4 20.6 3.4 17.2 3.4

4.5 Awareness of farmers on animal emission

Figure 11 shows that the majority of the urban and peri-urban dairy farmers in the milk shed believed that animals do not have any contributions to climate change. However, some of the farmers did not have any idea about climate change and the potential contribution of animals to climate change. From the sampled respondents, only a few of them (19.6%) knew about this issue.

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23 4.6 Economic analysis of milk production

Average milking cows and milk production

The data in table 15 shows the average number of milking cows, milk production, supply and price of milk in urban and peri-urban areas. On a household level, peri-urban dairy farmers maintained a significantly higher number of milking cows (8.3) than urban dairy farmers. The urban dairy farmers produced an average of 9260 litres per year from 3.6 cows, with an average lactation length of 7.7 months.

The result also shows that peri-urban farmers consumed significantly high volume (5.32 litres) of milk than urban farmers. On the contrary, Urban farmers supplied large volumes of milk to the market (39.2 litres) and consumed very little of the total produced. However, the price was not significant in both production system.

Table 15. Milk production and supply in urban and peri-urban farmers

Variable Urban (Mean ± SD) Peri-urban (Mean ± SD) P-valve Milking Cows/HH 3.6 ± 2.4 8.3 ± 9.1 .011*

Milk yield /Cow/day 12.01 ± 4.5 6.58 ± 4.2 .000*

Milk yield/ Household/ day 41.0 ± 42.9 25.8± 20.2 .035* Lactation Length /Cow (month) 7.7 ± 1.5 6.9 ± 1.2 .010* Milk yield/cow/year 2816.6 ±1246.6 1406.1 ±1018.1 .000*

Milk yield/Year/HH 9260 ± 9021.1 5504±4726 .041*

Home consumed milk/ day 1.89 ± 1.91 5.32 ± 6.14 .006*

Marketable milk/day 39.2 ±42.14 20.5 ± 20.0 .090*

Price of milk 18.2 ± 2.61 17.7 ± 4.60 .580

*significant difference at p<0.05 Daily milk Sales

As indicated in table 16, the daily milk sales of urban farmers were higher than peri-urban farmers. The independent sample test also confirmed the significant difference in the daily milk revenue in the two-production system.

Table 16.Daily milk sales in an urban and peri-urban production system Farming Average marketable

milk/day (litre)

Average selling price /day (ETB)

Daily total sales

(ETB) P-value

Urban 39.2 18.2 713.4 0.028*

Peri-urban 20.5 17.7 362.8

*significant difference at p<0.05 1Euro=32.13 ETB

The total cost of production per litre of milk

Table 17 explained the cost needed to produce one litre of milk. The result shows that urban and peri-urban dairy farmers incurred a total cost of 16.27 ETB and 17.11 ETB respectively and received different gross margin. On average, urban dairy farmers obtained higher gross margin (1.93 ETB) than peri-urban dairy farmers (0.59ETB).

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24

Table 17. Cost of production in urban and peri-urban production Farming system Milk yield

/cow/year

Total variable cost /year

The total cost of production/litre of milk

Urban 2816.6 45826.1 16.27

Peri-urban 1406.1 24058.31 17.11

1Euro=32.13 ETB

Revenue and Gross margin of milk production

As indicated in table 18, the yearly average milk revenue from a single milking cow was higher in urban farming. On average, urban farmers collected a higher yearly revenue of 51262.12 ETB than peri-urban farming who collected very less. These farmers incurred a high variable cost for producing a large volume of milk per year. These resulted in a higher gross margin per farm. On the contrary, the average revenue per cow indicates the low milk yield and high variable cost and consequently low gross margin per cow. Table 18. Average variable cost, revenue, gross margin in urban and peri-urban production

Parameters Urban Farming Peri-urban Farming

Average variable cost/liter 16.27 17.11

Average revenue/liter 18.20 17.70 Gross margin/liter 1.93 0.59 Average cost/cow/year 45827.22 24058.37 Average revenue/cow/year 51262.12 24887.97 Gross margin/cow/year 5434.90 829.6 Average cost/farm/year 150660.2 94173.4 Average revenue/farm/year 168532 97420.8 Gross margin/farm/year 17871.8 3247.4 1Euro=32.13 ETB

4.7 Climate smart selection of feed and financial cost evaluation

For achieving climate-smart dairy production, increasing the nutritional value of the feed and minimising the enteric rumen emission from the feed is very crucial. The study considered urban and peri-urban farmers as a separate category for calculating the emission value of the feed. From forage category, the majority of sampled respondents used local green grass and green maize forage with average feed purchasing cost per kg of 3.72 and 1.77 in urban and 2.84 and 0.96 ETB in peri-urban respectively. Moreover, these feeds have good metabolizable energy. So, giving these feeds to the cow will enhance the rumen digestibility and consequently took less rumination time and less enteric emission (Table 23). From crop residue category; wheat, teff and barley straw were principally used in both production system. However, the majority of farmers in the system were used wheat and teff. These feed types have relatively high metabolizable energy and CP value. Therefore, reduced the emission level of methane gas per kilogram of feed offered. These make them an appropriate crop residue to use for the two farming systems. However, barley straw has low metabolizable energy and high feed cost (Table 23).

Urban and peri-urban dairy farmers had a different preference for concentrate feeds. In the urban area, farmers used linseed meal, wheat bran, almi mixed ration, atella (local brewery byproduct) and brewery

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25

byproducts. These feeds have high CP and metabolizable energy which made a vital feed menu for the dairy animals. However, the purchasing cost of the feed shows that wheat bran, atella and brewery by-products were the three least-cost concentrate feed types.

Peri-urban farmers also used concentrate feed but in small proportion. As depicted in table 23, the percentage of farmers using concentrate feed was small. Concentrate feeds like linseed meal, wheat bran, lentil bran, Nug, atella have high CP and metabolizable energy. However, the market price was the limiting factor for the farmers. By looking at their metabolizable energy and relative least cost price, wheat bran, atella (local brewery residue), brewery grain and lentil bran were economically viable.

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26

Table 19. Nutritional value, emission and cost of different feeds in urban and peri-urban milk production system

Feed resources DM (% as feed) CP (% DM) GE (MJ /Kg DM) DE (MJ/Kg DM) ME (MJ/KG DM) Emission factor /kg /cow EquC02/ kg of feed/cow Feed cost/ kg (urban Feed cost/kg (Peri-urban) % of sampled farmers used the feed (urban % of sampled farmers used the feed (Peri-urban Green pasture

(Bermuda grass mainly) 31.3 9.8 18 10 8.1 0.021 0.526 3.72 1.07 23.5 31

Maiz green forage 23.3 7.9 18.2 11.7 9.6 0.0213 0.531 2.84 0.96 25.5 27.6

Cabbage 9 23 18 0.021 0.526 12 _ 1.96 1.96 Alfalfa 90.6 18.3 18 10.7 8.5 0.021 0.526 1.38 0.5 1.96 0 Wheat straw 91 4.2 18.5 8.3 6.8 0.0216 0.54 4.51 2.45 82.35 72.4 Teff straw 91.7 14.6 18.5 9.7 7.9 0.0216 0.54 2.55 1.49 33.33 17.2 Barley straw 90.9 3.8 18.2 8 6.5 0.0213 0.531 3.17 1.67 19.6 48.3 Maize stover 28.9 6.9 18.1 9.4 7.6 0.0211 0.529 2.5 3.4 1.96 17.2

Fagullo (linseed meal) 90.6 43.1 20.7 16.2 12.6 0.0242 0.604 10.8 11.20 78.43 48.3

Frushka (Wheat bran) 87 17.3 18.9 13.5 11 0.0221 0.552 6.6 5.88 84.31 69

Almi ration (Mixed

ration) 92.3 21 23 0.0269 0.672 8.6 9.00 50.98 6.8

Cotton seed meal

(cotton seed hull) 90.6 5.1 19.6 8.2 6.5 0.0229 0.572 11.34 8.34 1.96 10.3

Lentil bran 88.9 19.3 18.6 16.8 13.5 0.0217 0.543 0 3.45 0 3.45

Nug (Nug seed cake) 92.2 31.3 20.2 13.5 11.3 0.0236 0.59 0 4.00 0 6.9

Atella 15.6 20 19.9 11.72 10 0.0232 0.581 4.6 1 35.29 3.4

Sugar cane molasses 73 5.5 14.7 11.3 9.6 0.0172 0.429 0 2.81 0 10.3

Brewery by product

(Brewers grains) 91 25.8 19.7 12.4 9.9 0.023 0.575 1.43 2.1 15.69 6.8

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27

4.8 Opportunities and challenges of Milk production in Ziway-Hawassa milk shed Table 20. PESTEC and SWOT analysis of milk production

SWOT

PESTEC Strength Weakness Opportunities Threats

Political + Existence of development agents assigned in each district

- Limited support for extension service - Presence of livestock master plan - Political instability - Land shortage*** Economical + Growing economic

potential of farmers (urban farmers) + Better market access + Large cattle

population

-Low milk price/ litre -Poor market

infrastructure (in peri-urban)

- Price fluctuation - The high price of concentrate feed*** - The high cost of cross breed cows/heifers -Low bargaining power of milk producers - Increasing demand for milk Social + Availability of productive labour force -Limited coordination among milk producers

-Capacities for improvements are available

-long fasting season

Technological + Presence of feed processing plant in the shed (Alto)

+ Establishment of milk processing plant (e.g. Almi)

+ Presence of private cross breed heifer multiplication centre (Gobe farm) -limited improved forage seeds -limited milk processing technology plant - frequent AI failures -Presence of research centres, universities, NGO, livestock and fishery office -Presence of veterinary service - Animal disease

Environmental + Availability of water in the area

-Shortage of feed*** Availability of cattle manure (fertiliser) in the

area

-Climate variability

Cultural All family members are working in cooperation

-Dominancy of women in milk processing and milking

Cultural Taboo (Peri-urban)

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28

4.9 Business model canvas of the Ziway- Hawassa milk shed

The business environment of the study area was described in figure 11 and 12. The existing business environment of the study area was assessed based on its accessibility to inputs, labour, time and resource efficiency. Based on this, the current business model lacks some aspects of linkage and resource efficiency. So, to scale up climate-smart dairy, the scalable business model was suggested which link farmers to different activities and partners.

4.9.1 Business model canvas for urban dairy farmers Figure 11. Urban Business Model Canvas

Key Partners • Office of livestock and fisheries • ALMI • ADRC • Credit institutions • Energy and water development office • Vegetable and flower producing companies • Municipalities • Calf fatteners • Feed suppliers Hawassa University Key Activities • Animal feeding • Healthcare • Breeding • Proper Manure management Value Propositions • Quality milk • 1-3-month calves Customer Relationships • Maintain Agreement • Quality pricing Customer Segments Almi (shashemene & Negelle) Bereket wetet (Negelle) Other private cooperatives Key Resources • Land • Human power • Stables • Dairy cows Channels • Taking to collection points • Farmgate Cost Structure • Feed cost • Veterinary Cost • Breeding • labour Revenue Streams • Milk sell

• Old and sterile cows sell

• Sell of 1-3-month calves

Manure sell Social& Environmental Cost

2.01kgeqCO2/liter

Social & Environmental Benefit Reduce milk wastage Minimize greenhouse gasses

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29 4.9.2 Business canvas for peri-urban dairy farmers

Figure 12. Peri-urban Business Model Canvas

Key Partners • Office of livestock and fisheries • Gobe farms • ADRC • Bishofitu RC • Hawassa University • Credit institutions Energy and water development office Key Activities • Animal feeding • Healthcare • Breeding • Feed preservation • Feed production Crossbreeding Value Propositions • Quality milk • Processed dairy products Customer Relationships • Maintain quality Agreement Customer Segments • Individual customer • Cooperatives Key Resources • Land • Stables • Dairy cows • Human resource Channels • Taking to collection points • Farmgate Cost Structure Feed cost Veterinary Cost Breeding labour Revenue Streams Milk sell

male calves sell

Old and sterile cows sell Manure selling

Social & Environmental Cost 5.92 kg eq. CO2/litre

Social & Environmental Benefit Improved Soil fertility Carbon sequestration Source: Author Sketch (2018)

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30 5.

Discussion

This chapter presents the discussion of the research based on the results of the fieldwork and desk study. 5.1 Socio-economic Characteristics

The average family size shows that Peri-urban dairy farmers had higher family members than urban farmers. The large average size of this farming system especially in kofele district might be related to limited family planning as they want to have many children supporting their different crop-livestock related activities. Hence, having a large family size might increase social security in times of retirement. The agrees with Yitaye et al., (2008) who mentioned the same reason for the case of the dairy production system in North-western Ethiopian highlands.

The illiterate proportion of the current study was much lower than Weldeslasse et al., (2012) who reported 16.25% for both urban and peri-urban dairy farmers in the Central zone of Tigray, Northern Ethiopia. The better literacy of the current study might be due to better educational infrastructure which favours them to engage in primary education. These had a positive impact in enhancing societies to intensify their farming system and adopt new technology in improving dairy productivity.

5.2 The structure of milk value chain In Ziway-Hawassa milkshed

Different stakeholders like actors and supporters were involved in the existing milk value chain. The chain actors included were input suppliers, producers, small or large collectors, processors, retailers and consumers. As it was indicated in the chain map (Figure 7) the input suppliers were livestock and fishery office, alema, private drug suppliers, Adamitulu research center, Gobe farms and SEDA. They provide inputs like forage seeds, drugs, feed, improved breed and another service to the milk producers. A similar finding by Brandsma et al., (2013) also explained the input service providers and their service in the milk shed.

Milk producers were small holder dairy farmers living in urban and peri-urban areas of the milk shed. The majority of farmers were producing milk as a major enterprise activity and sell to small- or large-scale collectors. There were also famers cooperatives like Biftu who perform the milk production, collection and processing function. The chain identified that there were four marketing channels which link farmers to different market sources. Farmers were selling the milk at the farm gate or to milk collectors (small or large) or directly to consumers.

The collection function of the milk shed includes small- or large-scale collectors who collect milk on daily bases. There were also collectors who also process and retail milk at different retailing shops. Kiosks, milk bars and supermarkets were the primary retailing agents for institutional as well as local consumers. Different authors like Brandsma et al., (2013) and Tadesse (2016) were studied the different chain aspects in the shed.

5.3 Farming system and land size

Majority of the respondents in urban farming were following livestock production with milk production as a major agricultural activity. On the contrary, peri-urban farmers, especially living in kofele district, were practised mixed production. The result of this work agrees with Ayenew et al., (2007) and Weldeslasse et al., (2012) who reported 88.9% and 67.94% of urban farmers were following livestock

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