Characteristics of Urban and Peri-Urban Dairy Production

Characteristics of Urban and Peri-Urban Dairy Production Systems in Ethiopia

in Relation to Bovine Tuberculosis

Adam Bekele Tilaye Teklewold Mulualem Ambaw Stefan Berg Catherine Hodge Tadele Mamo

Research Report 125

የኢትዮጵያ የግብርና ምርምር ኢንስቲትዩት

Ethiopian Institute of Agricultural Research

Characteristics of Urban and Peri-Urban Dairy Production Systems in Ethiopia

in Relation to Bovine Tuberculosis

©EIAR, 2019

ኢግምኢ፤ 2012

Website: http://www.eiar.gov.et Tel: +251-11-6462633 Fax: +251-11-6461294 P.O. Box: 2003 Addis Ababa, Ethiopia

Copy editor: Abebe Kirub

ISBN: 9789994466665

Contents Preface 1

Foreword 2

Introduction 10

Methodology 13

Sampling 13

Data collection 14

Data analysis 14

Results and Discussions 15

Demographic and socioeconomic characteristics of

respondents 15

Demographic characteristics of farm owners 15

Farm ownership and land use pattern 16 Farm ownership 16

Land use pattern 17

Farm workers socioeconomic characteristics 18

Dairy farm establishment and maintenance 21 Herd structure 21

Trends in dairy farm establishment 24

Access to support services 26

Cattle selling and buying 29

Cattle management 35 Farm bio-security 39

Disease management 41

Milk production and processing 47 Herd level bTB prevalence 52

Farm ownership and bTB status 53

Farm herd size and bTB status 55

Farm bTB history 55

Multivariate analysis of risk factors for bTB incidence 55 Knowledge about zoonosis 58

Milk and meat consumption patterns and zoonotic risk 61 Household health care seeking behavior 71

Conclusions 74

References 77

1

Preface

This study was initiated to understand the socio-economic characteristics of dairy

farmers with the main objective of assessing the importance of bovine tuberculosis

and possible means of controlling it. The study was carried out on 480 sample dairy

farms located in urban and peri-urban areas of Ethiopia.

This study was funded by the Biotechnology and Biological Sciences Research

Council, the Department for International Development, the Economic & Social

Research Council, the Medical Research Council, the Natural Environment Research

Council, and the Defense Science & Technology Laboratory, under the Zoonosis and

Emerging Livestock Systems (ZELS) program, ref: BB/L018977/1. The authors are

indebted to the Regional Livestock Agencies and Organizations, Dairy co-operatives,

and Dairy farmers who collaborated and allowed this study to be performed.

The authors are greatly indebted to researchers representing different research

disciplines and academic institutions from Ethiopia, the UK and Switzerland for their

partnership and contribution in study design, field work and data analysis. These

include: Dawit Alemu, Henrietta L Moore, Stefan Berg, Chilot Yirga, Lijalem

Abebaw, Rea Tschopp, Adane Mihret, Getachew Gari, James Wood, Getnet

Mekonnen, Gizat Alemaw, Sintayehu Guta.

The authors also extend special thanks to the ETHICOBOTS consortium: The

Ethiopian Institute of Agricultural Research (EIAR), University College London

(UCL), Cambridge University (CU), Animal and Plant Health Agency (APHA),

Armauer Hansen Research Institute (AHRI), National Animal Health Diagnostic and

Investigation Center (NAHDIC), Aklilu Lemma Institute of Pathobiology/Addis

Ababa University (AAU), Swiss Tropical and Public Health Institute (Swiss TPH)

Authors

2

Foreword

Ethiopia aspires to see the fruition of significant opportunities that can arise in the

form of urban and peri-urban dairy intensification, increased employment and

emergence of wider markets for the millions of rural smallholder farmers and

commercial farmers seeking to make a living from livestock production. However,

there is a fear that intensification of the dairy industry in urban and peri-urban

production systems could lead to increased rates of livestock diseases and associated

zoonotic diseases, among which bovine tuberculosis (bTB) is one. Thus, a detailed

understanding of the urban and peri-urban dairy farming systems is crucial to the

development of appropriate, acceptable, and feasible bTB control strategies for this

sector. To this effect a field survey was conducted under the project titled “Ethiopia

Control of Bovine Tuberculosis” in urban and peri-urban areas of Ethiopia, with the

objective of collecting information on many aspects of dairy farming including: the

socio-economic characteristics of farm owners and farm workers, herd structure, herd

management, marketing, access to agricultural inputs in relation to bovine

tuberculosis.

The survey result illustrated that intensive farming could lead to higher risk of disease

transmission particularly in large, government and cooperative owned farms.

Prevalence of zoonotic disease was observed in animal population and consumption of

raw milk and raw meat by human beings has been prevalent. There have been

challenges of dairy product processing, marketing and information. All of these would

need appropriate policy design.

Hence, EIAR management believes that the results from the survey are vital to be used

as inputs for understanding the dairy-system operating in the urban, peri-urban, and

taking lessons for informed policy making in the dairy-development.

EIAR also duly appreciates the financial support of Biotechnology and Biological

Sciences Research Council, the Department for International Development, the

Economic & Social Research Council, the Medical Research Council, the Natural

Environment Research Council, and the Defense Science & Technology Laboratory,

under the Zoonosis and Emerging Livestock Systems (ZELS) program,

Mandefro Nigussie (PhD)

Director General

Ethiopian Institute of Agricultural Research

3

አኅጽሮት ይህ ጥናት ከከብቶች የሳንባ ነቀርሳ በሽታ ጋር በተያያዘ በከተማና በከተማ ዙሪያ ባለ ዋና ዋና የወተት ከብቶች የእርባታ እርሻዎች የግብርና ስርዓትን ሇማጥናት የታቀደ ነው፡፡ ጥናቱ በመቀሌ፣ ሀዋሳ፣ ጎንደር፣ አዲስ አበባና ዙሪያዋ ባለ 480 የወተት ከብት እርባታዎች ላይ የተከናወነ ሲሆን የባሇሀብቶቹንና በስራ ላይ የተሰማሩትን ሰራተኞች የማህበራዊና ኢኮኖሚያዊ ገጽታ፣ የወተት ከብቶች አደረጃጀትና አያያዝ እንዲሁም በበሽታዎች ላይ ያላቸውን ግንዛቤ ሇመረዳት ተችሏል፡፡ በዚህ መሰረት የወንዶች ባሇሀብቶችና ሰራተኞች ቁጥር ከሴቶች የበሇጠ መሆኑን፣ አብዛኛው ባሇሀብት የተማረ መሆኑን፣ የተቀጣሪ ሰራተኞች ከአንዱ ወደሌላው እርባታ በመቀያየር የመስራት እንቅስቃሴያቸው ከፍተኛ መሆኑን፣ በከተማ ውስጥ የወተት ከብት እርባታ ቦታዎች ጥበት ማጋጠሙን፣ ትላልቅ እርባታዎች እንዲሁም የመንግስትና የማህበር የወተት ከብቶች አያያዝ ሇበሽታ መከሰትና መተላሇፍ ከፍተኛ የስጋት ቦታዎች እንደነበሩ፣ አብዛኛው ተጠቃሚዎች ጥሬ ስጋ የመጠቀም ልምድ እንዳላቸውና ይህም ሇተላላፊ የከብቶች በሽታ ተጋላጭነታቸውን ከፍተኛ እንደሚያደርገው፣ ባሇሀብቶች ስሇከብቶች ሳንባ ነቀርሳም ሆነ በሽታው ከከብት ወደ ሰው እንደሚተላሇፍ ያላቸው እውቀትና የመቆጣጠሪያ ዘዴ አነስተኛ መሆናቸው እንዲሁም የወተት ከብቶች ግብይትም ሆነ የኤክስቴንሽን አገልግሎት ስርዓት ያልዳበረ እንደሆነ ታውቋል፡፡ ስሇሆነም እነዚህ ሁነቶች በወተት ከብቶች ዘላቂ ዕድገት ላይ የሚያስከትለት በጎና አለታዊ ተጽዕኖ ከፍተኛ መሆኑ ግንዛቤ አግኝቶ በዋናነት የመረጃ፣ የጤናና የግብይት አገልግሎትና ስርዓት የማሻሻያ አቅጣጫዎች ተነድፈው ተግባራዊ መደረግ ይኖርባቸዋል፡፡

Executive summary

High population growth and high rates of urbanization in developing countries such as Ethiopia

have contributed to increased demand for livestock products, which in turn offer significant

development opportunities within the livestock sector in general, and the dairy sector in

particular. These opportunities can arise in the form of urban and peri-urban dairy

intensification, increased employment and the emergence of wider markets for the millions of

rural smallholder farmers and commercial farmers seeking to make a living from livestock

production. Taking the growing and emerging demand for economic growth and the role of

livestock into consideration, the government of Ethiopia has prioritized the development of the

livestock sector.

While Ethiopia has a vast number of cattle (estimated at over 55 million heads), most of these

are local (zebu) breeds, which, while hardy and well suited to their environment, do not

produce high milk yields. For this reason, the government has encouraged aspiring dairy

producers to invest in the crossbreeding of zebu cattle with highly productive European breeds,

such as Holstein-Friesians. While some live animals have been imported from overseas, the

main source of these crossbreeds has been the government‟s Artificial Insemination service,

which is offered to farmers at a relatively low cost. Although these cross-bred cows are

considered to be far more productive than the local breeds, they have also been found to be

more susceptible to a variety of different diseases. They also require more water and more food

4

than local breed animals and are generally farmed in intensive systems where they are kept

indoors at all times.

Therefore, intensification of the dairy industry using urban and peri-urban production systems

could lead to increased rates of livestock diseases and associated zoonotic diseases like

brucellosis, listeriosis and bovine tuberculosis (bovine TB) that become very important

economic and public health threats due to an increased risk of disease transmission in such an

intensive environment. Thus, a thorough understanding of the urban and peri-urban dairy

farming systems is crucial to the development of appropriate, acceptable and feasible bovine

TB control strategies for this sector. Taking this into consideration, through the Ethiopia

Control of Bovine Tuberculosis Strategies (ETHICOBOTS) project, researchers representing

different research disciplines and academic institutions from Ethiopia, the UK and Switzerland

have entered a partnership to assess the prevalence of bTB and to explore ways in which bTB

might be controlled in the Ethiopian dairy sector.

5

Study Background

This report is based upon the results of a survey carried out across the Ethiopian urban

and peri-urban dairy sector between January 2016 and June 2017, encompassing 480

individual farms. The survey was designed mainly by a team of epidemiologists and

social scientists, including anthropologists and agricultural economists from the

Ethiopian Institute for Agricultural Research in Addis Ababa and the Institute for

Global Prosperity at University College London and sought to capture the socio-

economic characteristics of farms of three size categories defined as: Small (3-19

cattle), Medium (20-49 cattle), and Large (>49 cattle) farm. The work formed part of

the much larger ETHICOBOTS project that are working together to investigate the

prevalence of Bovine TB among Ethiopia‟s dairy cattle, to assess the zoonotic

potential of the disease and to advise the government and other key stakeholders on

potential strategies for control, surveillance, and prevention.

Objectives

The survey was designed with the objective of collecting information on many aspects

of dairy farming including the following topics

The farmers themselves, including age, gender, educational level, knowledge of zoonoses

and meat and milk consumption፤

The employed „farm workers‟;

The land available to dairy farms;

The structure of herds within the farms, how they are started, maintained and managed

through sales and purchases;

The management of cattle, including feeding and watering practices, biosecurity practices

and the management of disease cases;

Milk production, processing and use;

Access to extension and support services;

Cattle trade; and

Risk factors

While the survey was being carried out, veterinarians from the ETHICOBOTS project

were also carrying out tuberculin testing of cattle on the 480 sampled dairy farms,

therefore allowing researchers to combine the two datasets and identify potential risk

factors for bTB infection.

The survey was carried out across several study sites in Ethiopia, all of which were

selected by the project due to relatively high levels of dairy production activity in

urban and peri-urban areas. These study sites are

Gondar in Amhara Region;

Bishoftu/Debre Zeit, Sululta, Sebeta, Holetta Holetta and Sendafa, all in Oromia Region;

Addis Ababa in Addis Ababa Special Region;

Hawassa in Southern Nations Nationalities and Peoples‟ Region (SNNPR); and

6

Mekelle in Tigray Region

Key Findings

Analysis of the survey results generated the following findings that may prove

useful in the design of policy for the control and prevention of Bovine

Tuberculosis in the Ethiopian dairy sector:

Farmer/Farm Worker Characteristics Seventy-seven percent of the surveyed farmers were male and there was no statistical

relationship between gender and farm size, i.e. female farmers were just as likely to run

large farms as they were to run small ones. The majority (61%) of investigated farm

workers were also male;

84.9% of the farms were privately owned, 12% were cooperatives and 3.9% were

government owned. Most of the government owned farms were in the „large‟ size

category

Most farm owners (91%) were literate. However, 63.8% of farm workers had left

education at the end of primary school, or before

About 52% of the farm workers were family members of the farm‟s owner, while 48% of

them were hired, most of them at medium and large farms and

Employed farm workers moved frequently between farms; on average, they stayed 2 and

7 months in small and medium farms, respectively, but they stayed for longer periods of

time at large government farms, on average 36 months.

Structure of dairy farms, sales, and purchases of cattle Most farms were situated on less than 2 hectares of land;

Those farms with more land tended to use some of it to grow crops. Diversification

increased with the size of the farm;

The majority of barns at these dairy farms were constructed from corrugated iron sheet roof

and cemented floor;

Herds were dominated by heifers, cows and calves of cross bred cattle; in addition, larger

farms kept a higher rate of bulls and oxen, as they were more able to afford feeding animals

which do not produce milk; and

Herds were mainly restocked through breeding using artificial insemination or

own/borrowed bull, through purchasing animals, and through gifts from relatives.

When farmers sold cattle, they tended to do so at relatively low prices, suggesting that the

sales were not part of a long-term financial strategy, but were rather done so in response to

illness/low productivity and/or in order to meet immediate cash needs. The majority of

sales were made to slaughterhouses.

Access to services Respondents described limited agricultural extension services, which tended to focus on

animal husbandry. Twenty-five percent of those with access to extension services

reported having received training on bTB;

Sixty-four percent stated that they had access to Artificial Insemination services.

However, many respondents stated that the service was ineffective and of poor quality,

both in terms of personnel and in terms of the quality of semen being used;

7

Most of the respondents (97.5%) had access to veterinary services, with 51.4% of large

farms, 31% of medium farms, and 25% of small farms relying on private clinics. Many

large farms employ their own vet, either solely for their farm, or as a group of farms;

Veterinary services were described as inadequate, with public/government services being

inefficient and private clinics being expensive and lacking basic drugs and facilities.

Farmers also stated that they must take animals to the veterinary clinics, rather than the

vets coming to the farm. This was found to be difficult to do and can compromise

biosecurity;

Sixty-seven percent of farms had access to credit, but only 19.7% had borrowed any

money in the last 5 years. Microfinance institutions were the dominating supplier of credit

to surveyed dairy farmers; and

Forty-nine percent of respondents indicated, when asked, that the government did not

provide adequate support for the dairy sector in Ethiopia.

Dairy cattle selling and buying Selling and buying were performed occasionally and for mainly destocking (59%)

followed by culling due to diseases (14), scarcity of space (14%) and immediate cash

need (12%);

Dairy cattle sold were 3 times as much as bought and high-blood calves (mainly male)

were more frequently sold than bought;

High blood heifers and cows were the two main animal categories that were sold due to

diseases;

The greatest percentage of high blood animals that were sold went to slaughter-houses

followed by cattle traders and neighboring farms; and

Improved cows followed by heifers were the most commonly bought animals and dairy

farms and traders were the main suppliers of dairy cattle.

Animal management and care Cattle were most commonly fed and watered 2-3 times a day although some were given

free access to water;

Regarding feed, the dairy farms spent on average most money on hay and oil cake

(fagulo); the most used concentrates were molasses, cake, brewery by-products,

formulation rations, and wheat bran;

Most farms bottle-fed calves with bulk milk and/or milk from their mother;

Sixty percent of farms provided their animals with separate troughs for water and feed;

and

To improve the hygienic condition of the farm, it was common practice of cleaning the

barn by using water after removing the dung from the floor. Most of the dairy farmers

accumulate the dung nearby the farm because of lack of enough space for dung removal

from farm to elsewhere.

Knowledge and management of animal and zoonotic diseases Ranking the impact of diseases among the dairy farmers, Mastitis was found to be the

number one severe and economically important disease followed by Foot and mouth and

Lumpy skin diseases. Other common problems included viral diseases of cattle and

infertility;

Farmers seek to manage cattle disease through a combination of: vaccination, isolation

and quarantining, seeking veterinary treatment, using traditional medicines and, as a last

resort, selling or culling cattle who are displaying symptoms of disease;

8

Farmers tended to be aware of „human to human‟ and „animal to animal‟ transmission of

TB, but not of its zoonotic potential. They also had more knowledge of TB in humans

than of TB in cattle (bovine TB); and

When asked about common animal health problems, 83 of the 480 farmers mentioned

bovine TB. Of these, 13% said that they would respond to an animal showing symptoms

of the disease by seeking veterinary treatment, 8% said they would segregate the animal

from the rest of the herd, 7% said they would sell the affected animal, but the vast

majority (65%) said that they would do nothing.

Milk marketing Buying and selling milk and other dairy products is a very challenging business,

especially during Ethiopian Orthodox fasting periods when prices drop very low.

Sometimes it is impossible for milk to be sold during the fasting periods and producers

and processors respond by processing raw milk into products with a longer shelf-life such

as cheese and butter;

There is little demand for processed milk and consumers generally prefer to buy raw milk

directly from the producers as they trust that the quality (as perceived by them) of the

milk will be higher and contamination is less likely; and

Mean milk prices per liter across the whole study sample ranged from 10.5 birr when sold

to cooperatives to 16.5 birr when sold directly to consumers.

Meat and milk consumption habits While 77.4% of respondents stated that they never drank raw milk, 81.8% reported

consuming fermented yoghurt („ergo‟), which is made from raw milk. 88.9% said that

they never drank pasteurized milk;

The most popular form of milk amongst respondents was boiled milk, which 89.1% drank

at least once a week;

Meat consumption within the study sample was higher than the average national

consumption rate. Meat consumption was higher among male-headed households than

female-headed households. 56.7% of farmers ate meat 2-5 times per week;

The majority of the surveyed farmers (63.9%) ate raw meat, either with 20.5% saying

they do so every day or 2-5 times per week. Reported rates of raw meat consumption

were considerably lower (25%) in Mekelle than in any of the other study sites, which

ranged from 66.5% of surveyed farmers in Addis Ababa City to 76.5% of those surveyed

in the Oromia towns surrounding Addis Ababa; and

92.9% of farmers believed that eating raw meat could lead to catching a disease and

around 40% had experienced illness, which they attributed to raw meat consumption.

BTB prevalence and possible risk factors for bTB infection When the results of this survey were viewed alongside those from the tuberculin

testing of cattle carried out on the sample dairy farms, analysis revealed the following: Overall, on 46.4% of the farms, at least one animal tested positively for bTB using the

standard OIE tuberculin test;

The highest rate of herd positivity was found in Addis Ababa city, where 63.3% of tested

farms were found to be positive. Hawassa had the lowest proportion of positive farms, at

only 11.1% of the sampled farms. Mekelle and the Oromia towns surrounding Addis

Ababa also had relatively high rates of bTB in the tested farms;

Farms which were privately owned had lower rates of bTB positivity (43.9%) as

compared to government owned (66.7%) and cooperatively owned (54.7%) farms;

9

The risk of testing positive for bTB also seemed to increase with herd size. Of large

farms, 75.6% tested positive, along with 64.1% of medium and 37% of small farms;

The prevalence of bTB was significantly higher in farms practicing feeding of calves with

bulk milk than in farms that were feeding calf with dam milk or that allowed the calf to

suckle; and

Other factors, which seemed to increase the risk of a farm testing positive for bTB

included: possible contact with wild animals and other animal species and a lack of

farmer‟s training on zoonosis.

Implications for policy design Farms at highest risk of infection seem to be large (>49 cattle), government and

cooperative owned farms where cattle are reared intensively leading to higher risk of

disease transmission. Given the relatively low number of large farms efforts to control

bTB need to focus on these farms and would be cost effective if focus is geared towards

these farms;

Despite that 71% of the surveyed farms claimed their farm was completely enclosed and

that only 9% suggested access to wildlife, the risk of wildlife being a risk factor for

bovine TB came out significant. Therefore, biosecurity as a major intervention area need

to be emphasized in bTB control programs.;

The veterinary and the AI services were considered by farmers to be limited; therefore,

one area of intervention would be strengthening these services in terms of effectiveness

and customer satisfaction;

Cattle sold to slaughterhouses could be associated with the possibility of selling diseased

animals either intentionally or due to lack of awareness. Thus strengthening animal

marketing regulatory system, increasing the awareness level of farmers and provision of

veterinary services are essential;

Sources of dairy cattle varied by distance and type of cattle and the market for dairy cattle

is not well developed, implying the need for promoting efficient and accessible dairy

cattle buying and selling systems;

Rates of raw milk and raw meat consumption vary across regions, which is unsurprising

given the cultural diversity of Ethiopia. However, this behavior is alarming given the

prevalence of zoonotic diseases in the animal population. Therefore, government need to

induce safe behaviors in terms of consumption through public awareness programs and

also strengthen abattoir inspection capacity to detect infected meat.;

The appetite for pasteurized/processed milk from consumers is very low, meaning that

there is little incentive for producers to sell to processors, or for processors to set up

business. This calls for deliberate effort to promote milk processing as well as processed

milk products;

Farmers face challenges in milk marketing, particularly during fasting periods, leading to

very low milk prices and high wastage. Market regulation, and/or increased investment in

processing might enable farmers to invest more in infrastructure and biosecurity

measures; and

Most farmers are literate, but farm workers, who often carry out practical animal

management tasks, particularly on large farms, tend to have lower levels of education

overall. Communication and education campaigns should consider this fact. Awareness

should be created among dairy producers about milk, feed and water borne diseases

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Introduction Most of the developing countries in the world, and in Sub-Saharan Africa in

particular, are experiencing accelerated economic growth over the last decades (World

Bank, 2017). This growth has increased per capita incomes, which is causing

increasing consumption of animal products such as milk and meat (Delgado, 2003).

Increased population growth and high rates of urbanization being witnessed in

developing countries have also contributed to increased demand for livestock

products. According to Delgado (2003), who called this phenomenon „the livestock

revolution‟, the demand for livestock, meat and milk in sub-Saharan Africa will

increase by between 3.2 and 3.3% per year between 1997 and 2020. More recent FAO

projections up to 2030 and 2050 suggest similar growth estimates for these products in

Africa (FAO, 2013).

The situation in Ethiopia is also similar and the Ethiopian Livestock Development

Master Plan predicted a 53% deficit in meat supply and 24% deficit in milk supply as

compared to demand by 2028, in the absence of appropriate interventions (LMP,

2015).

These circumstances offer significant development opportunities within the livestock

sector in general and the dairy sector in particular. These opportunities arise in the

form of urban and peri-urban dairy intensification, increased employment and the

emergence of wider markets for the millions of rural smallholder farmers and

emerging urban and peri-urban commercial farmers seeking to make a living from

livestock production. Increased demand for livestock products not only offers value

chain development opportunities for businesses, but also calls for government

intervention to harness the possible economic gains in the form of increased national

income and export earnings from the sector, as well as to mitigate the possible risks to

human, animal and environmental health that have been shown to accompany

intensification in other contexts.

Taking this into consideration, the government of Ethiopia has prioritized the

development of the livestock sector and has developed a livestock development master

plan aimed at contributing to the fulfillment of livestock development targets in the

second Growth and Transformation Plan (GTP II, 2015-2020). In this master plan, the

Ethiopian dairy sector is given utmost priority over other alternatives such as beef and

poultry production and aspires to raise milk production levels by 93% by the year

2020 through genetic, feed, and health improvement of the traditional systems (LMP,

2015).

However, the dairy sector faces multifaceted challenges in the different production

systems; i.e. traditional or commercial livestock systems. Poor genetic potential, poor

feeding and animal husbandry, as well as poor veterinary care plague the traditional

system, which is characterized by low productivity in extensive smallholder or

pastoral settings. In addition to these factors, pervasive market failure in the form of

low price, fluctuating and uncertain demand, poor infrastructure and inadequate policy

11

and institutional support have kept the sector subsistent and underdeveloped. In

contrast, the more commercially driven husbandry system is largely urban and peri-

urban based on intensive dairy farming. Unlike those operating in the traditional

system, farmers engaging in „modern‟, commercialized methods of dairy production

have been able to access animals which are bred for and therefore genetically suited to

commercial milk production. They also benefit from better access to market and

support services, such as credit and genetic improvement programs such as Artificial

Insemination (AI) services and livestock identification. However, farms which operate

using these systems also suffer from their own problems, including, but not limited to:

limited supply and escalating price of feed, fluctuating demand for milk products, low

competitiveness in the face of imported products, lack of access to land, challenges of

waste disposal, and inadequate veterinary services. The animals used to produce milk

and its products in this system appear to be more susceptible to a number of disease

including bovine tuberculosis than the local breeds kept by rural and pastoralist dairy

farms (Vordermeier et al., 2012).

In the context described above, the intensification of the dairy industry using urban

and peri-urban production systems could lead to increased rates of livestock disease

and an associated increase in the burden/risk of zoonotic diseases. With increased

intensification, zoonotic diseases like bovine tuberculosis (bTB) become very

important economic and public health threats due to an increased risk of disease

transmission in such environment (Ameni et al 2007). In the absence of adequate

control strategies, the damage caused by these diseases can be huge (Cousins, 2001).

Some studies indicate that herd prevalence of bTB in urban and peri-urban intensive

dairy cattle production systems in Ethiopia could be as high as 50% (Ameni et al

2001; 2007; Elias et al 2008; Firdessa et al 2012) in large areas. Considering this,

Ethiopian researchers and researchers from the UK and Switzerland representing

different research and academic institutions have started partnership through the

Ethiopia Control of Bovine Tuberculosis Strategies (ETHICOBOTS) project to assess

the prevalence of bTB and explore ways to control bTB in the Ethiopian dairy sector.

The Project aims at providing scientific evidence and understanding for to developing

sustainable control strategies for bTB and associated zoonotic diseases in the dairy

sector of Ethiopia. The project, in collaboration and direct involvement of the different

Ministries (such as MoA, MoH) and farmers who have stake in dairy development and

disease control, is expected to identify and recommend possible bTB control strategies

that can contribute to reducing the high rate of bTB and its zoonotic transfer in the

expanding dairy sector.

A thorough understanding of the urban and peri-urban dairy farming contexts where

bTB prevalence is high is crucial to the development of appropriate, acceptable, and

feasible bTB control strategies. For the social scientists working within the

ETHICOBOTS project, developing such an understanding entails investigating and

documenting the socioeconomic characteristics of dairy producers and farm workers,

farm management practices and available marketing and support services. It also

entails developing an understanding of the farmers‟ and farm workers‟ knowledge and

perception about bTB and other zoonotic diseases, their milk and meat consumption

12

habits, and their health service seeking behavior. This research report, based on a

survey of dairy farmers across the ETHICOBOTS research sites, aims at describing

and explaining the urban and peri-urban dairy production system in Ethiopia in terms

of these features. The following section elaborates on the study‟s methodology and is

followed by a third section presenting results and discussion. The final section of this

report draws conclusions and makes recommendations based on the data from the

survey and the foregoing discussions of that data.

13

Methodology

Sampling Multistage sampling was followed to identify sample dairy farms. In the first stage,

four urban centers (study sites) were identified with known high level for dairy

farming activity; these were Mekelle in Tigray Region, Gondar in Amhara Region,

Hawassa in the Southern Nations, Nationalities and Peoples‟ Region, and Addis

Ababa,. The Addis Ababa study site consisted of Addis Ababa City and the five peri-

urban districts of Debre Zeit, Sululta, Holetta, Sendafa, and Sebeta.

Figure 1. The study areas investigated in ETHICOBOTS. The largest study site comprised the established dairy belt in Central Ethiopia, including Addis Ababa city and surrounding towns in Oromia. An additional three study sites were represented by the emerging dairy centers in Hawassa, Gondar, and Mekelle.

In the second stage of sampling, 480 farms were identified from within the study sites,

using cluster sampling techniques: First, an inventory of dairy farms in the identified

urban and peri-urban areas was taken from district level branches of the Office of

Livestock and Fisheries and were updated and verified through personal contacts with

key informant experts. These farms were grouped into large farms (with herd size

greater than 49 dairy cattle), medium farms (herd size between 20 and 49 dairy cattle),

and small farms (herd size between 3 and 19 dairy cattle). Thrusfield‟s (2007) formula

was adopted to determine samples in each stratum, considering each site as a unique

population having different variance. Samples were then identified using simple

random sampling technique. The sampling distribution is provided in Table 1. The

Gulf of

Aden

Somalia

Sudan

Kenya

Eritrea

Djibouti

South

Sudan

N

0 150

km

Gondar

Hawassa

Addis Ababa

&

surroundings

Mekelle

14

survey sites were also stratified into three clusters, namely smallholder (3-19) farm,

medium herd (20-49) farm, and large herd (>49) farm. In total 480 dairy farms from

the survey sites were included in this study.

Table 1: Dairy farm sample size

Study site Farm size clusters

Small-herd (3-19) farm

Medium-herd (20-49) farm

Large-herd (>49) farm

Total

Addis Ababa City 123 34 7 164

Sebeta 16 9 4 29

Holetta 24 8 2 34

Sululta 12 6 3 21

Sendafa 17 4 4 25

Debre Zeit 13 9 7 29

Gondar 53 9 4 66

Mekelle 50 8 2 60

Hawassa 29 19 4 52

Total 337 106 37 480

Data collection Survey data was collected through a well designed and tested questionnaire (Basic

questionnaire 1 (BQ1)) by trained, both male and female, enumerators with the

supervision of ETHICOBOTS researchers. Along with survey questions, dairy farm

cattle were tested for bovine TB, using the Single Intradermal Comparative Cervical

Tuberculin (SICCT) test (OIE, 2009) with PPD-A/B sourced from Lelystad (The

Netherlands). The questionnaire was prepared and tested by a multidisciplinary team

composed of agricultural economists, anthropologists, veterinarians and other

biomedical scientists. Computer Assisted Personal Interview (CAPI) equipment was

used for data collection. The questionnaire was designed to collect socioeconomic

characteristics of farmers and farm workers, farm management practices, knowledge

and attitude towards bTB and other zoonotic diseases as well as milk and meat

consumption behavior of farmers, farm workers and their families. Farm owners or

managers were the respondents during the interview that typical took about one and

half hour. In very few cases farmers refused to answer the questions or interrupted and

turned back the enumerators, otherwise most of the farmers were cooperative and

willing to answer the questions as well as allow bTB test of their animals.

Data analysis Descriptive techniques such as measures of central tendency and dispersion (mean,

median, standard deviations) and inferential statistical techniques such as t-test,

ANOVA, chi-square-test and others measures of association were employed to

analyze the data. Frequency tables, pie charts, bar graphs were used to pictorially

present data. STATA, SPSS, and MS-Excel statistical packages were used for data

analysis. We also used logistic regression for multivariate analysis of risk factors for

herd level incidence of bTB.

15

Results and Discussions

Demographic and socioeconomic characteristics of

respondents Of the total respondents, 64.6% were farm owners and 31.6% were farm managers

while the remaining 3.8% were employees. 76.3% of the respondents were male and

23.7% were female. The proportion of female respondents in small, medium, and large

farms, respectively, was 25.8%, 16.0%, and 27.0% (Table 2). The mean age of the

respondents was 46.37 years (standard deviation +/-14.54). With regard to educational

status, 92.9% of respondents were literate. The median number of years of experience

on the current farm for the respondents was 6 years. These characteristics could

indicate that, given that the respondents were mostly owners or managers of the farm

(96%), they had high percentage of literacy rate, were middle aged and with

reasonable number of years in the farm, the data collected from the respondents could

be interpreted as reliable.

Table 2: Respondents demographic information in percentage

Characteristics Herd size of dairy cattle

Small-herd (3-19 cattle)

Medium-herd (20-49 cattle)

Large-herd (>49 cattle)

Total (% & N)

Respondent's sex

Female 25.8 16.0 27.0 23.7 (114)

Male 74.2 84.0 73.0 76.3 (366)

Total (n=480) 100.0 100.0 100.0 100.0 (480)

Respondent's position

Manager 28.5 29.5 66.7 31.6 (150)

Owner 69.7 64.8 16.7 64.6 (306)

Employee 1.8 5.7 16.7 3.8 (18)

Total (n=474) 100.0 100.0 100.0 100.0 (474)

Respondent's education

Illiterate 8.9 3.8 0.0 7.1 (34)

Religious education 4.2 3.8 2.7 4.0 (19)

Primary education 32.9 21.7 10.8 28.8 (138)

Secondary education 31.5 28.3 16.2 29.6 (142)

Higher education 22.6 42.5 70.3 30.6 (147)

Total (n=480) 100.0 100.0 100.0 100.0 (480)

Demographic characteristics of farm owners Out of 428 farm owners, 76.6% of them were male while 23.4% of them were female.

In every farm size category, at least 23% of the farm owners were female (Table 3).

No systematic statistical relation was observed between sex of the owner and farm

size, indicating that irrespective of farm size both male and female owners are

engaged in dairy farming in similar proportions.

About 9.1% of the farm owners were illiterate while 4.2 % of the farm owners have

only attended religious education. The proportion of the dairy farm owners who

reported that they had completed primary, secondary, and higher education were

28.5%, 30.8%, and 27.3%, respectively. A statistically significant association was

observed between an owner's level of education and farm size (LR Chi (2) = 42.971;

16

P=0.000). The proportion of owners of large dairy farms who attended higher

education was 63.6%, while the corresponding figures for the medium and small farms

were only 37.9 and 20.5%, respectively. The higher education levels among large farm

owners could be because large and intensive dairy farming in urban and peri-urban

dairy farming system is knowledge and capital-intensive venture that the less educated

often lack (Table 3).

Table 3: Dairy farm owner demographic information in percentage

Characteristics Dairy farm size (number of cattle) LR Chi2

small-holder (3-19) farm

medium-herd (20-49) farm

large-herd (>49) farm

Total % (N)

Farm owner’s sex

Female 23.4 23.0 24.2 23.4 (100)

0.021 Male 76.6 77.0 75.8 76.6 (328)

Total 100.0 100.0 100.0 100.0 (428)

Farm owner’s education

Illiterate 9.7 5.7 12.1 9.1 (39)

42.971 ***





Total 100.0 100.0 100.0 100.0 (428)

The average age of the dairy farm owner was 50.5 years (SD +/- 13.76) while the

average age across the farm size categories was 50.4 years for small farms, 49.8 years

for medium farms, and 53.0 years for large farms. This age difference was not found

to be statistically significant. However, the average age of female and male farm

owners across all farm sizes, were 48.0 and 51.2 years, respectively, and this was

found to have a statistically significant difference. The implication could be that

female farmers are younger than male farmers and that the latter might have started

business earlier than the former.

Farm ownership and land use pattern

Farm ownership Figure 2 demonstrates the percentage of dairy farms owned by cooperatives, the

government, and private and/or corporate businesses in each farm size category. Of all

farms surveyed, the vast majority (84.9%) was privately owned in the form of sole-

proprietorship or a limited share company, while of the remaining farms, 12% were

cooperatives, and 3.9% were government owned. The small farmers were mainly

either privately owned or belonged to cooperatives; the medium and large farms were

either private or government owned or a small proportion (5.7%) of them was found to

be cooperative businesses. The government-owned farms were mostly ranches

established for genetic conservation, semen production, heifer production, teaching,

and research.

17

Figure 2: Farm Ownership by farm size

Land use pattern The survey results showed that, in one-year, dairy farms operated an average of 1.33

hectares of land (Ha) with standard deviation of 3.48 (Table 4). The average area of

land operated by large farms was the highest (1.97 Ha) followed by small farms (1.32

ha) and medium farms (1.11 Ha). The dairy farms allocated the highest share (0.41 ha)

to grazing land, followed by non-irrigated cropland (0.34 Ha), pasture land (0.28 Ha)

and irrigated cropland (0.15 Ha). Herd level results showed that small farms allocated

higher proportion of their land for grazing followed by pasture and non-irrigated crop

farming. Medium farms allocated higher proportion of their land to grazing followed

by non-irrigated crop farming. Large farms‟ allocation of land was considerably

different from the other two farm categories. These farms tended to allocate higher

proportion of their land for non-irrigated, irrigated, and grazing purposes.

Table 4: The operational land size in hectares for the last 12 months

Land type Small herd n=337

Medium herd n=106

Large herd n=37

Total n=480

mean SD mean SD mean SD mean Sd

Grazing land 0.40 1.56 0.43 1.65 0.47 0.97 0.41 1.54

Pasture land 0.33 1.85 0.15 0.71 0.19 0.69 0.28 1.60

Barn land 0.06 0.26 0.05 0.12 0.04 0.09 0.06 0.22

Office area 0.08 0.57 0.03 0.07 0.04 0.06 0.07 0.48

Non-irrigated cropland 0.31 1.18 0.32 1.23 0.66 2.10 0.34 1.29

Irrigated cropland 0.12 0.66 0.11 0.53 0.58 2.32 0.15 0.89

Other (unused) land 0.03 0.17 0.01 0.10 0.01 0.04 0.02 0.15

Total land operated 1.32 3.43 1.11 3.16 1.97 4.66 1.33 3.48

Operated land area is positively related to farm size, except that large farms allocated

more land for crop production than for grazing and pasture. Cattle feeding

requirements guide the land allocation priority of the farms. The implication could be

that, as farm size increases, the likelihood of diversifying and intensifying the farms

into other income generating activities such as dairy farm compatible businesses is

inevitable and sources of cattle feed (grazing land and pasture land) are very

important.

18

Farm workers socioeconomic characteristics Of the total farm workers, 60.62% of them were male while 39.38% of them were

female. The proportion of female farm workers in small, medium, and large farm,

respectively, was 21.49%, 15.47%, and 19.94%. These percentage differences were

not found to be statistically significant.

About 17.7 % of the farm workers were illiterate while 4.6 % of the farm workers

have only attended religious education. The proportion of the farm workers with

primary, secondary, and higher education was 41.5%, 24.4%, and 11.8%, respectively.

In each farm size category, the highest proportion of the farm workers was those who

had attended primary school education (Table 5). About 52.2% of the farm workers

were family members of the farm‟s owner, while 47.8% of them were hired. The

proportion of family member farm workers across the farm size categories was 68.1%

for small farm, 36.8% for medium farms, and 6.5% for large farms and the proportion

of hired farm workers across the farm sizes was 31.6% for small farm, 63.8% for

medium farm, and 93.5% for large farm (Table 5). As expected, the small farmers

depend much on family labor and the large and medium farms have more than 50% of

their work force as hired workers.

Table 5: Farm workers’ demographic characteristics (in percentage)

Variable Herd size

Smallholder (3-19 cattle)

Medium herd (20-49 cattle)

Large herd (> 49 cattle)

Total % (N)

Sex Female 42.52 35.07 27.59 39.38 (176)

Male 57.48 64.93 72.41 60.62 (271)

Total (n=447) 100.0 100.0 100.0 100.0 (447)

Education Illiterate 17.1 15.9 21.6 17.7 (424)





Total (n=2395) 100.0 100.0 100.0 100.0 (2395)

Employment status

Family member 68.1 36.2 6.5 47.8 (1045)

Hired 31.9 63.8 93.5 52.2 (1143)

Total (n=2188) 100.0 100.0 100.0 100.0 (2188)

The average age of the dairy farm workers was 33.6 years while the average age

across the farm sizes was 33.9 years for small farms, 34.1 years for medium farms,

and 32.3 years for large farms (Table 6). There was no statistically significant age

difference among the different herd size groups. The average earning per month for

farm workers was Birr 1012.6 with standard deviation of 532.3. The average earning

for farm workers in small farms was Birr 914.6, while it was 1135.1 and 1416.2 in

medium and large farms, respectively. This mean difference in earning by farm

workers across farm size categories was found to be statistically significant (F=19.09;

P=0.000). This could be due to the fact that the large farms are mostly registered

businesses, which may hire professionals and are required to pay standard wages

based on the existing labor market, while medium and small farms are usually

unregistered, often employing an informal and casual work force which are not usually

19

made up of professionals and who are paid lower amounts based on individual

negotiations and existing informal labor market prices.

The number of cattle per worker for the entire sample was found to be 6.5 (SD=+/-

4.6). The smaller farms had an average of 5.5 animals per worker while the medium

and large farms had an average of 8.6 (SD=+/-5.3) and 9.7 (SD=+/- 6.2) animals per

worker, respectively. This difference was found to be statistically significant. This

could be explained by the fact that the medium and large farms could be more capital

intensive than the smaller ones making labor more efficient and replacing some labor

with capital.

Our data also showed that the average number of months a hired worker stayed in the

farm was found to be 6.14 with standard deviation of 17.68. Comparison of mean

number of months of stay in the farm among the farm sizes was found to be

significantly different. On average, a hired labor stays in the farm for 1.67 months in

small farms and this number increases to 7.37 and 35.61 months, respectively, for

medium and large farms. This indicates that hired workers in large farms stay longer

because these are in many cases permanent positions in a large government owned or

corporate farms with better wages and conditions as compared to small and medium

farms which are often family farms hiring casual labor without permanent tenure and

poor wages and working conditions. The data also showed that farm workers stay

longer in government farms much longer (35.6 months) than in private (5.4 months)

and cooperative farms (3.8 months). This difference was found to be statistically

significant (F=15.60; P=0.000). This relationship holds true even after controlling for

farm size. The reasons behind might be related to the fact that employment in

government farms is mostly a permanent tenure. This implies that employees working

in government owned farms, which tend to be large and mostly with high bTB

prevalence, are more exposed to bTB infection risk than those working in private

farms and are mostly transitory.

Table 6: Farm workers’ descriptive information

Item Statistic Herd size of dairy cattle F Value

small-holder (3-19 cattle)

medium-herd (20-49 cattle)

large-herd (>49 cattle)

Total

Number of farm workers (hired) Mean 1.14 3.30 11.27 2.38 335.08***

SD 1.09 3.09 6.89 3.72

Number of farm workers (family) Mean 2.44 1.82 0.78 2.17 6.02***

SD 2.24 3.07 1.35 2.44

Earnings of hired farm workers per month in Birr Mean 914.6 1135.1 1416.2 1012.6 305.84***

Sd 486.6 473.4 734.4 532.3

Age Mean 33.9 34.1 32.3 33.6 503.82*

Sd 15.1 14.8 12.4 14.5

Number of cattle per worker Mean 5.5 8.6 9.7 6.5 28.53***

Sd 3.8 5.3 6.3 4.6

Number of months in the farm Mean 1.67 7.37 35.61 6.14 73.38***

SD 17.68

Similarly, our data showed that there was significant difference among farm sizes in

terms of number of hired and family workers (Table 7). As the large farms could not

20

rely on family labor or as government or cooperatives could own them, they used a

greater number of hired labor than did the small farms.

With regard to the risk of exposure to bTB, theoretically it is known that long

exposure to the microbe will increase the risk of infection (Regassa et al, 2008). Our

data showed that hired farm workers in bTB positive farms stay longer on that farm

with mean number of months of 8.35 months and standard deviation of 22.49 months

while those in bTB negative farms the mean number of stay for farm workers was

only 3.70 months with standard deviation of 9.4. Given that the hired workers are

mostly employed in large farms and a higher proportion of, the large farms are bTB

positive and given that these workers stay longer in these farms, they are highly

exposed to bTB. We also found a statistically significant association between the

existence of confirmed TB cases among farm workers and herd bTB status

(likelihood-ratio chi2 (1) = 5.8470 Pr = 0.016). Among the farms which reported

confirmed TB cases in the last three years among farm workers, 71.43% have herds

that are bTB positive (Table 7). However, this has to be confirmed with careful case-

control study using molecular techniques.

Table 7: Relationship between confirmed human TB infection history and farm bTB status

Occurrence of confirmed TB case in the farm in the last five years’ time

Frequency Herd bTB status Likelihood ratio Chi2

Negative Positive Total

No Count 247 200 447

5.84** Percent 55.26 44.74 100

Yes Count 6 15 21

Percent 28.57 71.43 100

Total Count 253 215 468

Percent 54.06 45.94 100

Analysis of the demographic characteristics of the farm owners revealed that there is

no significant gender and age disparity in ownership of farms; however, educational

gaps are significantly observed among the owners across farm sizes indicating that

large farms which are knowledge and capital intensive in nature are often ventured by

better educated farmers than the smaller ones. The relationship between farm herd size

and bTB prevalence implies that one the one hand, the large farms could be the

sources of infection in the absence of strong surveillance and cattle movement control

mechanism; on the other hand, since the number of the large farms is relatively small,

focusing on these farms in terms of bTB control could be cost effective and could have

a significant impact in terms of halting the spread of the diseases.

With regard to type of farm ownership in urban and peri-urban dairy systems, the

small farms are mainly either privately owned or cooperatives; the medium and large

farms are either private or government owned and a very small proportion of them are

cooperative businesses. The incidence of low bTB positive proportions among

privately owned small farms as compared to the cooperatively owned ones also

indicates that, in addition to farm herd size, bTB control strategies need to take into

21

consideration ownership and associated incentives and disincentives of bTB control to

the farmers.

Given that, hired workers are mostly employed in large farms and that a higher

proportion of the large farms are bTB positive and given that, these workers stay

longer in these farms, these workers have a high risk of exposure to bTB. In addition

to this, the data also showed that farm works stay longer on government owned large

farms, which often also tend to be bTB positive. On top of this, the fact that the vast

majority of the farm workers are either illiterate or have only a few years of schooling

implied that zoonotic TB control strategies need to give due attention to this segment

of farm works and ensure that policies and public information campaigns are

accessible to these demographic characteristics.

Dairy farm establishment and maintenance

Herd structure Results of analysis of our survey data on herd structure by breed type shows that about

90% of the cattle in the investigated herds are crosses between exotic (mainly

Jersey/Holstein Frisian) breed dairy cattle and Zebu, with high blood crosses and

medium-to-low blood crosses. The remaining cattle in these herds were of the local

zebus (Figure 3). Cross breeds with medium-to-low blood of exotic breeds represent

the highest share (73%) followed by those with high blood (17%) and local types

(10%) of the cattle on sampled farms. On medium sized farms there is a relatively

higher proportion of high-blood cross breeds and a lower proportion of local breeds.

However, there was no statistical significance difference between cattle groups and

farm size.

Figure 3: Herd structure by type of breeds and farm size categories

The herd structure by cattle type showed that cows (30%), calves (26%) and heifers

(female animals that have not yet had a calf) (23%) take the main share (79%) of the

total herd whereas bullocks (1-2yrs old), steers (9%) and bulls (12%) make up the

remaining 21%. The order was also consistent across the three farm size categories

(Figure 4). There was statistically significant difference at 1% level between and

22

within the different types of cattle and farm size (F=46.28, 91.14, 85.49, 124.2, 7.06 in

the case of calf, heifers, bulls/bullock, cows and bull/oxen) justifying the

independence of the observed share of each cattle type in the small, medium and large

farms.

Figure 4: Herd structure by type of cattle and farm size categories

Further analysis of herd structure in terms of the high-blood (exotic and cross bred)

cattle categories shows that cows and calves dominate (about 30%) in small herd

farms followed by medium and large farms (Figure 5). However, the share of bullocks

(oxen) and bulls was in reverse order with the large farms taking the lead (about 13%

and 16%) followed by medium farms (about 10% and 15%) and small farms (about

5% and 9%). This situation was statistically significant at 1% level across the three

farm sizes. The main justification could be that large farms tend to integrate advanced

farm activities like keeping bulls and bullocks, respectively, for breeding and selling

purposes by taking advantage of their financial and business position compared to the

other types of farms.

Figure 5: Herd structure of high-blood (exotic and cross) breeds by farm size

On the contrary, the local zebu cattle herd structure was not definitive as in the case of

high blood dairy cattle (Figure 6). The proportion of local cows and calves was high in

the large farms (about 35% and 22%, respectively). The reason could be short supply

of exotic or high-blood breeds and better access to local breeds. Though not

significant, the proportion of local oxen was marginally high (about 38%) in the case

of medium farms. This could be due to the practice of fattening of local oxen as

supplementary income source, which is common in medium farms.

23

Figure 6: Herd structure of local breeds by category of cattle

Other animals including dogs, cats, sheep and goats, equines and poultry also live with

the dairy cattle with different degrees of contact (Figure 7). Considering the response

rate of 59.8% of the respondents, the possible contact of cats, dogs, equine, sheep and

poultry is high. Few farms (less than 3%) reported that goats and swine have some

possibility of contact with dairy cattle. The overall response of the farms on the

possibility of contact between other animals and dairy cattle was highest in small

farms (41%), followed by medium farms (14%) and large farms (4.8%). The

implication is that dairy farms unanimously keep different animals along with their

cattle and the risk of being exposed to zoonotic diseases, if they exist, can be evident

in all the farm types with increased probability as farm size decreases.

Figure 7: Possibility of contact of dairy cattle with other animals

Evidence from the statistical analysis above indicates that herd structure in terms of

breed type varies only by proportion of different breed types and not by type of farms,

whereas the herd structure in terms of generic category of cattle varies by farm size.

High-blood cows, heifers, and calves represent higher shares in small and medium

farms whereas oxen and bulls make up more of the herds in the large farms. This

could indicate the relatively stronger economic position of large farms that allows

them to carry out advanced type of dairying, enabling them to feed and keep animals

that do not themselves produce milk. Dairy farms also keep other animals (such as

dogs) and the possibility of contact between these animals and dairy cattle is high.

Such contact could increase the chance of cattle becoming infected with zoonotic

24

diseases, particularly on small farms where the likelihood of contact seems to be

higher. Provision of investment capital and farm bio-security models may be

necessary to promote an improved dairy farming.

Trends in dairy farm establishment According to the respondents, the dairy farms were established mainly by the farm

owners themselves (60%), through purchases of established farm (28%), by

inheritance (7%), and by gifts from relatives (5%). The decadal history of dairy farms

revealed that the established dairy farms have shown an increasing trend since 1955

(Figure 8). Taking all other factors as constant, the linear association of the number of

dairy farms established and time of establishment indicates that the decadal addition of

new dairy farms was about 40. The coefficient of determination (R2=0.857) also

shows strong association of the data on the number of dairy cattle and the time factor

to estimate the trend.

Establishment of medium farms was at its peak during the 1998-2007 periods,

whereas the number of small farms was consistently increasing over the whole period

since 1955. Recession was observed in terms of medium and large farm establishment

in the most recent decade. Thus, the general increase in the number of dairy farms can

be attributed to small farms. This indicates that large farms might have faced

difficulties of expanding in the cities compared to the other farm types. Possible entry

and business barriers to large farms could be limited access to land, high value of land

and feed shortage that resulted from increased urbanization and economy boom in and

around cities. Conversely, such a scenario coupled with the development of

cooperative dairy farms helped small farms flourish better.

Figure 8: Number of farms established per decade since mid 1960ies (Gregorian calendar)

The trend in the establishment of dairy cattle farms in the urban and peri-urban areas

(i.e. the study areas) shows a consistent but slow increase over the last decade. This

increase was observed among both small and medium farms and not among the large

farms. This could explain the relatively better prospect in these areas for establishing

the two types of farms than for the large farms, which in turn could be the result of

25

limited access to land in urban and peri-urban areas for expanding large farms in these

areas.

Means of starting a dairy farm Analysis of survey data on how farmers started their dairy farms showed that farms

were established through purchases of new stock (60%), purchasing of an existing

farm (28%), inheritance (7%) and other means, such as gifts from relatives (5%).

Thus, the establishment of new farms and purchasing of existing farms were the main

means of starting dairy farms in the study areas. There was no statistically significant

difference by farm size, implying that practice was similar across farm size categories.

Methods of restocking of dairy cattle Dairy farms used different methods to restock their cattle herds (Table 8). However,

the choice of method for restocking depends on the ease of that farmer‟s access to a

particular method. Accordingly, 89.4% of respondents reported restocking their herds

by breeding using AI, 69% purchased live animals, 42.5% breed using their own bull,

39.6% breed using a bull from another farm, 10.6% restock through the government

breed improvement program, and 4.6% have received animals as gifts from relatives.

Thus, it is clear that among all of the methods, the contribution of the government

program and of the gifting of animals was very low while the role of AI and

purchasing of live animals were very significant methods of restocking. Farmers‟

responses were statistically significant at 1% level by farm size in terms of bull and

government related service provides strong evidence that the distributional differences

in terms of responses of the farms vary by farm size types and hence decisions on the

use of these two methods is independent of herd size. However, use of farms‟ own

bull increased with farm size, use of bulls from other sources tended to decrease with

increasing farm size and the use of government breeding sources appears to follow a

different pattern; i.e. it is higher in the case of large farms followed by small and

medium farms. On the other hand, there was a lack of statistically significant

difference between the different farm sizes and farmers‟ responses on AI, purchasing

of live animals, and the receipt of gifts, indicating the similarity of each method of

restocking of dairy cattle in terms of their role.

Table 8: Methods of restocking of dairy cattle (% response) by farm size

Methods of restocking (N=480) Small-herd (3-19) farm


Large-herd (>49) farm

Total X2/F-test

AI No 11.3 9.4 8.1 10.6 0.555

Yes 88.7 90.6 91.9 89.4

Own bull No 66.5 38.7 29.7 57.5 38.134***

Yes 33.5 61.3 70.3 42.5

Purchasing No 31.8 26.4 37.8 31.0 1.937

Yes 68.2 73.6 62.2 69.0

Bull from another farm No 54.0 71.7 86.5 60.4 21.947***

Yes 46.0 28.3 13.5 39.6

Gift No 95.3 94.3 100.0 95.4 2.079

Yes 4.7 5.7 - 4.6

Government breed improvement program

No 89.9 92.5 75.7 89.4 8.472***

Yes 10.1 7.5 24.3 10.6

26

In summary, the dairy farms were established predominantly through the purchasing

of new stock. Purchasing of old farms was also important. Artificial insemination

played a major role in the restocking of dairy herds. Though the government provided

the AI service, its role was not significant. Thus, favorable policy provisions in terms

of creating access to land and infrastructure and provision of alternative and reliable

sources for starting and replenishing herds should be put in place to promote the dairy

farm establishment and the restocking of dairy cattle.

Access to support services Access to support services such as credit facilities, extension advisory services,

artificial insemination and veterinary services are important for sustainability,

increased intensification, and productivity as well as for the prevention of animal and

zoonotic diseases.

Access to credit The majority of these respondent farmers (67.2%) indicated that they have access to

credit facilities; however, only 19.6% of them actually borrowed money for their

farms in the last five years. Analysis of the relationship between farm size and access

to credit facilities indicated that there is no statistically significant relationship

between the two factors, indicating that regardless of farm size there is a fair level of

access to credit support services. However, a statistically significant relationship exists

between access to credit facilities and farm ownership (Chi2=14.012; P=0.007). It was

the cooperative farms, which had the highest levels of access to credit facilities

(87.7%) while the private farms indicated that 65.0% of them have access to support

services. This is because government credit support service focuses much on

promotion of youth, women, and farmer cooperatives for dairy farms.

With regard to the source of credit, 76% indicated that microfinance institutions are

their main source and 22.9% indicated that commercial and/or development banks are

their main source of credit. This, in fact, is dependent on farm size and our data

showed that from the smallholders only 12. 3% indicated that their main sources of

credit are commercial banks while only 36% of the respondents from medium farms

and 83.3% of those from large farms indicated the same. The main sources of credit

for the small and private farms were found to be microfinance institutions followed by

banks and informal sources such as local moneylenders. For the cooperative farms, the

main source of credit was microfinance institutions followed by

commercial/development banks.

Our data showed that the smallholder farmers on average borrowed 141,050 birr in the

last three years, with the minimum and maximum being 4,500 and 650,000,

respectively (Table 9). As expected, the medium and large farms borrowed on average

472,783 Birr, with the minimum and maximum being 20,000 and 350,000. This

difference was found to be statistically significant (t=-2.17; P=0.04).

27

Table 9: Credit (Birr) borrowed in birr by farm size

Holding category Mean Minimum Maximum SD

Less than 20 cattle (n=44) 141,050.07 4,500 650,000 164,396.01

Greater than 20 cattle (n=23) 472,782.61 20,000 3,500,000 723,066.95

Extension and veterinary services With regard to access to extension service, the majority of respondent farmers (74.3%)

indicated that they have access to extension services and that their main source of

extension advisory is government extension service (69.8%). However, only 25.9% of

them indicated that they have accessed any extension service related to zoonotic

diseases prevention and control; similarly, 25.1% indicated that they had received

training on bovine TB. With increasing intensification and disease burdens, this lack

of extension advisory on animal health and disease prevention may increase the risk of

zoonotic transfer of diseases to farm owners, farm workers and dairy consumers.

Access to training was found to be significantly different among the various farm

ownership types (LR Chi2=9.5; P=0.009). Of those farms owned by the cooperatives,

73.29% indicated that they have access to livestock husbandry related training; the

corresponding figure for the private farms was found to be 60.2% and for government

owned farms it was only 33.3% (Table 10).

Table 10: Distribution of dairy farm access by farm ownership

Farm ownership Access to Training on Livestock Husbandry

Likelihood Ratio Chi2

No Yes

Private Count 154 236

9.5002**

Percent 39.8 60.2

Government Count 10 5

Percent 66.67 33.33

Cooperative Count 18 50

Percent 26.71 73.29

The respondent farmers were asked to describe the frequency of their contact with

extension agents in any given month and the average figure was found to be 2.37

times with standard deviation of 3.1. No statistically significant difference was

observed in mean frequency of contact with extension agents among farm sizes.

However, a statistically significant difference was observed in the mean frequency of

contact with extension agent among regions (F=14.145; P=0.000). Famers in Addis

Ababa reported a higher frequency of contact with extension agents per month (mean=

3.8; SD=3.5), followed by Mekelle with mean value of 2.00 and SD of 1.8; the lowest

mean frequency of contact with extension agents was recorded in Hawassa, where the

mean reported monthly frequency of contact was 1.29 with SD of 1.08.

With regard to AI services, 64.44% of respondents indicated that they have access to

AI services. However, it is often indicated that the AI service lack quality that it is

often not effective. As a result, there are frequent instances of failure of conception

28

and need for repeated insemination. This is mainly due to lack of adequate skill to

detect heat and administer semen properly as well as due to poor semen quality.

Comparison of „farms access to AI services‟ by „farm ownership‟ also showed that

there is a statistically significant difference (LR Chi (2)= 4.630; P=0.099). Of the

privately owned farms, 36.32% indicated that they don‟t have access to AI service

while the corresponding figure for the cooperative farmers was found to be only

26.47% (Table 11). This might be due to increased government focus on cooperative

farms at the expense of privately owned ones. This indicates that government support

services such as AI, Extension, Credit and vet services are more directed to

cooperatives than the private farms.

Table 11: Distribution of dairy farm access by farm ownership

Farm ownership Access to AI Likelihood Ratio Chi2 No Yes

Private Count 142 249

4.630*

Percent 36.32 63.68

Government Count 8 7

Percent 53.33 46.67

Cooperative Count 18 50

Percent 26.47 73.53

Veterinary services are one of the important services often sought by farmers,

especially in an increasingly intensifying system. Of the total sample of farmers

surveyed, 97.5% indicated that they have access to veterinary service. The main

source of veterinary services in all the study areas was found to be the public vet

service (70.92%) followed by the private sector (28.45%). A few farmers indicated

that they treat their sick animals themselves and none indicated that they seek

traditional healers to deal with animal diseases. The average distance to the nearest

veterinary office, be it private or public, was found to be 4.2 km. Our data also showed

that the majority of large farms (51.4%) rely on private vet services, while the

corresponding figure for the medium and smallholder farms were 31.1% and 24.9%,

respectively. In fact, in many cases, the large farms either have a resident or hired

veterinarians who serve the farm on an on-call basis.

However, the public and private veterinary services are not without problems. The

public veterinary service was often ill equipped, with insufficient supplies of drugs.

Farmers also indicate that they have to bring their sick animals to the clinic and is not

of much help as it is not a door-to-door service. They also indicated that the public

service is plagued by nepotism, corruption and a lack of trained and skilled staff. On

the other hand, the private sector is reported to be efficient, providing door-to-door

services on an on-call basis, yet it is expensive and, like the public vet service, lacks

drug supplies and an adequately skilled workforce. Farmers were also asked if they

feel that the government is supporting the dairy sector adequately. The result indicates

that 49.1% do not believe that there is adequate support.

Credit facilities were biased in favor of small and cooperative owned farms while the

medium and large privately owned farmers lack credit support services and access to

29

formal bank loans is often limited due to collateral requirements and high interest

rates.

The limited extension service available was also more focused on general animal

husbandry giving less emphasis to zoonotic diseases including bTB. With increasing

intensification and disease burdens, this lack of extension advisory on animal health

and disease prevention may increase the risk of zoonotic transfer of diseases to farm

owners, farm workers and dairy consumers. Door-to-door provision of veterinary

services can also improve farmers‟ access to the services and improve their knowledge

of management of animal diseases including the zoonotic ones.

The veterinary services available in the study areas were reported to be inadequate.

The public/government veterinary service was inefficient and marred by corruption,

while private services were expensive and lack basic drugs and facilities. Again, with

increasing intensification, the lack of adequate private or public veterinary service

could lead to increased diseases burden on the cattle and the farmers as well.

Cattle selling and buying

Selling Table 12 shows the dairy farm owners‟ participation in selling animals in a particular

year. The result indicates that the sampled dairy farms sold all types of (exotic and

local) cattle. The average number of cattle sold was 3 (SD=3.48). The number of

sampled farms who sold cattle was sequentially high on high blood cows, calves, and

bulls. In terms of average prices, cows generated the highest income, followed by

bulls, heifers, and calves. The average transaction cost of selling animals ranged from

nil for calves to more than 700 birr for adult animals. The cost of brokerage increased

with the prevailing high demand for a particular animal. Thus, in this case, there was

higher demand for cows and heifers and the associated transaction costs were high.

The possible reason for selling cattle could be either income generation as in the case

of cows and heifers or culling of old cattle or destocking.

Table 12: Animals sold during one-year period (2015-2016)

Type of cattle sold Cattle sales Cost paid for brokers and communication

(Birr/animal) No. of sellers

Average cattle sold

Average price (Birr/animal)

High blood cows 206 3 18833 723

High blood bulls 97 2 18464 352

High blood heifers 52 1.8 17425 607

High blood calves 191 3 3030 180

Local cows 13 2 9184 100

Local bulls 16 4 9156 163

Local calves 4 2 2275 -

Local heifers 4 2 6250 150

Total 364 3 10788 411

30

Cattle purchases Dairy farms‟ participation in cattle purchasing is described in terms of cows, heifers

and bulls purchased (Table 13). The total number of farms participating in cattle

purchasing in the year before the survey took place was 129 (26.8% of all the sampled

farms), with a range of4 to 86 farms depending on the type of cattle bought in a year

period. The number of buyers was highest for high blood cows and heifers. The

average number of cattle bought by the buyers was 3 (SD=3.51). The price of high

blood dairy cows was the highest, followed by high blood heifers. The price of

purchased cows and heifers was higher than that of sold cows and heifers which may

be attributed to price and quality differentials that would exist between the two groups,

i.e. cattle sold could be those with inferior quality (destocked) sold for any buyer and

those purchased naturally were of superior quality purchased by farms for restocking

purposes. The transaction costs for brokering a single animal ranged from 123 birr for

local cows to 928 birr for high blood cows, while transportation costs ranged from 55

birr for local cows to 549 birr for high blood dairy cattle. The main reason for such

variation in transaction costs between the local and high blood cattle could be the

difference in the relative market prices of the two categories and the trading distance.

Table 13: Animals bought and costs (Birr/animal) during one-year period (2015-16)

Type of cattle bought Cattle purchases Cost paid for broker and

Communication (Birr/animal)

Transportation cost

(Birr/animal) No. of buyers

Average no. of cattle bought

Average price (Birr/animal)

High blood cows 86 2.6 30182 928 549

High blood bulls 10 1.6 10350 320 108

High blood heifers 39 1.8 22411 607 387

Local breed cows 4 17 4750 123 55

Local breed bulls 13 5 6854 131 332

Local breed heifers 4 2 11625 567 325

Total 129 3 18642 605 342

Sales and purchases The market participation of surveyed farms was assessed in terms of the number of

cattle sold and bought (Table 14) and in terms of the number of farms participating in

the sales and purchases of cattle (Table 15). Thus, the market participation of the dairy

farms in a year in terms of the number of cattle shows that the number of cattle sold

(1,589) was higher than the number of cattle bought (445). In fact, the number of

cattle sold was over three times higher than the number bought showing a general

trend of selling cattle, remotely likely to compensate for sold out animals and/or to

maintain herd size. This scenario was also observed when the data is broken down by

type of animal, except in the case of local cows, local bulls, and local heifers. High-

blood (exotic-breed/cross-breed) calves were sold far more frequent than they were

bought, in order to bring in profit. High blood calves were sold mainly for

slaughtering and for traders.

These calves were usually sold if they were male and kept for breeding if they were

female. However, unexpectedly, the net selling of exotic breed/cross-bred heifers was

31

shown to be greater than the net purchasing in the year preceding the survey time

(Table 14). The reasons for this, besides keeping the herds constant, would be either

for destocking (according to 50.7 percent of the farms) or the market demand was

high.

Table 14: Animals bought and sold during one-year period

Animal Bought Sold

Total Mean Total Mean

High blood cows 222 2.6 609 3

Local breed cows 69 1.5 20 1.5

High blood bulls 16 1.6 184 1.9

Local breed bulls 61 4.7 58 3.6

High blood calves - - 614 3.2

High blood heifers 71 1.8 92 1.8

Local breed calves - - 6 1.5

Local breed heifers 6 1.2 6 1.5

Total 445 1589

In terms of farmers‟ participation in cattle marketing, Tables 15 and 16 depict that a

number of factors precipitated participation. The main reasons for selling high blood

(exotic breed/cross-bred) cows were destocking (59%), culling due to sickness (14%),

immediate need for cash (12%), profit making (7%), and other reasons (11%) such as

shortage of barn space (Table 15). The main reason given by farmers for buying dairy

cattle was for replenishing herds (Table 14). Although there are different reasons that

lead the sampled dairy farm owners to sell their cattle, the cattle sold were made up of

a high proportion (76%) of high blood (exotic-breed/cross-bred) calves (probably

males), followed by high blood (exotic-breed/cross-bred) bulls and cows (58% and

55%, respectively) each were sold to destock while local heifers sold for the same

reason account to 50%. The result also indicates that high blood heifers (10%) and

high blood cows (13%) are the top two ranked animal categories that were sold due to

diseases, which could be linked to claims that exotic breeds are more susceptible to

diseases. Local calves, high blood heifers, and local bulls are the top three ranked

animal types that were sold to solve immediate cash needs (Table 15). There were also

other reasons why animals were sold which include low milk yield, old age, infertility,

and lack of barn space or feed, as well as to replace them with better breeds. Overall,

over 50% of the dairy farm owners sold animals to destock (reduce farm size) and to

solve immediate cash needs. Selling due to the animal being sick and „other reasons‟

accounted for less than 10% of each of sales made.

32

Table 15: Reasons for selling dairy cattle in 2015/16

Animal Total number of farms participating

in selling (N)

To destock

Culled due to sickness

Immediate cash needs

For profit making

Other reasons

n % n % n % n % n %

High blood cows 206 113 55 26 13 24 12 15 7 28 13

Local breed cows 13 6 46 1 8 3 23 2 15 1 8

High blood bulls 97 56 58 4 4 17 18 18 19 2 2

Local breed bulls 16 2 13 1 6 5 31 6 38 2 13

High blood calves 191 145 76 1 1 5 3 35 18 5 3

High blood heifers 52 19 37 5 10 21 40 3 6 4 8

Local breed calves 4 1 25 - - 2 50 1 25 - -

Local breed heifers 4 2 50 - - 1 25 - - 1 25

Total (frequency) 444 242 54 35 8 65 15 64 14 38 9

Note: Total number of farmers engaged in selling=365. Each farm sold cattle for at least 1.2 purposes making the frequency of participation 444

Some 129 farm owners (27% of the total 480 surveyed) were involved in cattle

purchasing. These farm owners bought dairy cattle for restocking or expanding the

existing farm (86% of them), for resale (10% of them) and other purposes (4% of

them) (Table 16). Eighty-two of the 480 farm owners said they purchased high-blood

(exotic breed/cross-bred) cows and 37 bought high-blood heifers for restocking. Bulls

of local breed were bought for resale purpose, i.e. income generation.

Table 16: Reasons for purchasing animals

Animal type No. of buyers

To restock /expand farm

For resale Other reasons

n % n % n %

High blood cows 86 82 96 2 2 2 2

Local breed cows 4 - - 3 75 1 25

High blood bulls 10 7 70 2 20 1 10

Local breed bulls 13 4 31 8 61 1 8

High blood heifers 39 37 95 1 3 1 2

Local breed heifers 4 4 100 - - - -

Total (frequency) 133* 114 86 14 10 5 4

Note*: Total number of farmers engaged in buying=129. Each farm purchased cattle for at least 1.03 purposes making the frequency of participation=133

The surveyed dairy farm owners reported selling their cattle to different buyers. Figure

9 presents the proportion of animals sold to different buyers. The result indicates that

the greatest percentage of high blood (exotic breed/cross-bred) animals that were sold

(cows 34.5%, bulls 51.5%, and calves 50.5%) went to slaughterhouses. Cattle traders

were the major buyers of local bulls (56%) and a significant proportion of high blood

heifers and local calves (50%) were sold to neighboring farms. Overall,

slaughterhouses, cattle traders, and neighboring farms were the top three buyers of

animals from the surveyed dairy farm owners (Figure 9).

33

Figure 9. Destination (sales route) of animals sold (%) Note: far-off would indicate distances beyond 10kms

Descriptive results on why farm owners were involved in purchasing animals and the

different sources of purchased animals show that, except for the cows and bulls of

local breeds that were mostly purchased for resale, the most common reason for

purchasing all other types of cattle was to restock or expand the farm (Figure 10).

Although relatively rare, some farm owners bought improved heifers and local cows

for breeding and local bulls for draft purposes. In general, as indicated in Figure 10,

restocking (highlighted in blue) followed by reselling (highlighted in red) were the

most common reasons for buying dairy cattle by the sampled farms. It is also clear that

improved cows followed by heifers were the most commonly bought animals.

Figure 10. Reasons for buying dairy cattle

Figure 11 shows the sources of animals purchased by the sampled dairy farms in a

year. It depicts that farms situated far away (>10kms) from the respondent‟s farm,

followed by neighboring farms were the two most common sources for purchasing

improved cows, bulls and heifers. However, neighboring farms were the main source

(75%) for the supply of local heifers. Traders played an important role in supplying

bulls and local cows. The role of government, though small, was focused on supplying

with improved cows. Overall, the two types of farms (neighboring and distant farms)

account for 76% of the sources from which the dairy farm owners purchase cattle,

34

followed by traders who supplied about 14% of the cattle bought. The share of all

other sources of purchased animals was less than 10% (Figure 12).

Figure 11: Sources of dairy cattle bought (%)

Figure 12. Sources of purchasing animals

In spite of often having years of marketing experiences and being at close proximity to

market centers, cattle farm owners‟ participation in trading of dairy cattle has been

low (about three times in a typical year). The prices at which animals were sold were

relatively low, indicating that dairy cattle trades were generally not driven by market

forces (supply and demand) or instigated by the desire to make profit. Sales were

generally made because of the need to destock or to liquidate assets in order to meet

cash needs. The participation of buyers was high in terms of the purchasing of high

blood dairy cattle (cows and heifers), indicating a higher level of demand for these

animals. The fact that dairy cattle were often sold to slaughterhouses indicates that

cattle were often sold for non-breeding purposes. This appears to substantiate the idea

that culling has taken place on these farms either due to low productivity or due to

disease symptoms observed in the cattle sold. Sources of cattle varied by distance and

type of cattle This implies the need for developing efficient and accessible buying and

selling systems.

35

Cattle management

Feeding and watering Feeding and watering are the most important duties performed by producers to care for

their animals and to maximize milk production and productivity. Adequate feed and

water is also important to maintaining animals‟ ability to resist infection and disease

(Mulligan et al., 2006). Different feed types were used in the study dairy farms,

including: wheat bran, salt, brewery by-products, hay, crop residue, molasses and

noug cake (locally called, fagulo), which were the most commonly available feed

types in the study areas. The majority (>50%) of the dairy farms used wheat bran,

brewery by-products and salt regardless of farm size. Figure 13 represents the

percentage distribution of feed type in different farm size categories. The result is

similar to that observed by Gebrekidan and Gangware (2015) who conducted similar

studies on dairy feed in Northern Ethiopia.

Figure 13: Usage of feed type (%) in three different farm size categories (n=number of farms represented).

Zero grazing, partial grazing and free grazing are practiced by 81.25%, 18.54% and

0.21% of the investigated dairy farms, respectively (Table 17). The result is similar

with the previous research findings conducted on urban and peri-urban dairy

production systems (Shiferaw et al 2003, Guendel 2006, Kagira and Kanyari 2010,

Dayanandan 2011). Free grazing was very rare and only observed among smallholder

dairy producers while partial grazing was highest (35.1% in relative proportion) in the

large farms.

Table 17. Feeding of dairy cattle by grazing in the study areas

Feeding by grazing Herd size Total

Small Medium Large

Zero grazing 283 (83.98%) 83 (78.3%) 24 (64.86%) 390 (81.25%)

Partial grazing 53 (15.73%) 23 (21.7%) 13 (35.14%) 89 (18.54%)

Free grazing 1 (0.3%) - - 1 (0.21%)

Total 337 (100%) 106 (100%) 37 (100%) 480 (100%)

The vast majority of the dairy farm owners gave roughage feed for their dairy cattle

either two times a day (46.3%) or three times a day (41.5%). In this survey,

36

supplementing the roughage feed with concentrate was shown to be a common

practice. Most of the dairy farms gave concentrate supplements twice a day (68.5%)

while 28.8% gave it three times a day. Only a few dairy farm owners gave roughage

(4.6%) and concentrate supplements (2.3%) once a day to their cattle (Table 18).

Free access feeding (feeding cattle without specific schedule within a day) practice

was not common in the study dairy farms as only 7.7% of all surveyed dairy farms

practiced such feeding of roughage. However, this practice was slightly more common

among the large dairy farms (13.5%). Unlike the extensive system in rural areas,

small-scale dairy producers were dependent on purchased feed and gave concentrate

as supplementation during milking.

Table 18: Feeding schedule for roughage and concentrate in the study dairy farms

Feeding schedule Small-herd Medium-herd Large-herd Total

Roughage

All time (free access) 26(7.7%) 6(5.7%) 5(13.5%) 37(7.7%)

Three times a day 144(42.7%) 43(40.6%) 12(32.4%) 199(41.5%)

Two times a day 150(44.5%) 54(50.9%) 18(48.7%) 222(46.3%)

Once a day 17(5.0%) 3(2.8%) 2(5.4%) 22(4.6%)

Total 337(100%) 106(100%) 37(100%) 480(100%)

Concentrate supplement

Three times a day 111(33.0%) 21(19.8%) 6(16.2%) 138(28.8%)

Two times a day 214(63.7%) 84(79.3%) 30(81.1%) 328(68.5%)

Once a day 10(3.0%) 1(0.9%) 0(0.0%) 11(2.3%)

Every other day 1(0.3%) 0(0%) 1(2.7%) 2 (0.4%)

Total 336(100) 106(100%) 37(100%) 479(100%)

In terms of usage of feed and water troughs, all farms provided water in troughs to

their cattle and 99% of the farms did the same with feed. However, 1% used no

feeding trough at all (Table 19). About 68% of the dairy farms used separate troughs

(one trough per animal), 25% of them used common trough for all dairy cattle, while

6% used one trough per two animals. Common troughs, especially for water, were

more commonly used in medium and large farms. Contagious disease transmission

from cattle to cattle can occur during feeding and watering with common troughs, so

using separate trough for each animal can help for the control of contagious diseases.

Table 19: Use of water and feed troughs in dairy farms

Troughs Small-herd Medium-herd Large-herd Total

Water troughs

Separate trough for each animal 248 (73.6 %) 60(57.1 %) 20(55.6 %) 328 (68.6 %)

Common trough for all animals 80 (23.7 %) 39 (37.1%) 15 (41.7 %) 134 (28.0 %)

One trough for two or more animals 9 (2.7 %) 6 (5.70 %) 1 (2.8 %) 16 (3.4 %)

Total 337 (100 %) 105 (100%) 36 (100%) 478 (100%)

Feeding troughs

Separate for each animal 230 (68.3 %) 70 (66.0 %) 24 (66.7 %) 324 (67.6 %)

Common trough for all animals 68 (20.2 %) 29 (27.4 %) 9 (25.0 %) 106 (22.1 %)

No feed trough 6 (1.8 %) 1 (0.9 %) - 7 (1.5 %)

One trough for two or more animals 33 (9.8 %) 6 (5.7 %) 3 (8.3 %) 42 (8.8 %)

37

Total 337 (100 %) 106 (100 %) 36 (100 %) 479 (100 %)

Watering Dairy cattle require a lot of water and must drink frequently in order to maintain their

body function and milk production. The major sources of water for the dairy cattle in

the study areas were from taps, wells, rivers, and streams; most of the dairy farms used

water from tap (74.5%), followed by well water (18.2%). These figures reflect the fact

that most of the surveyed dairy farms were located in urban areas, but that some are in

peri-urban areas where tap water supply is limited. Well-water was used by a larger

proportion of large farms (44.4%) than by medium (30%) and small (14%) farms, and

verifies the location of large farms in the peri-urban situation. Obviously, the

availability of tap water is restricted in urban areas, whereas usage of well, stream and

river as water sources are found widespread in peri- urban areas.

Regarding the frequency of watering, dairy cattle were given both scheduled and free

access water in the study areas (Table 20). The majority of the farm owners provided

water to their cattle one to two times per day (67.3%) and the remaining 36.7%

provided water three to four times per day, or free access. Watering two times, a day

was most common whereas watering four times a day was least common.

Table 20: Source of water and frequency of watering for the dairy cattle in the study areas

Small-herd Medium-herd Large-herd Total

Water source

Tap water 264 (78.3%) 7 4 (70.5%) 18 (50%) 356 (74.5%)

Well water 47 (14%) 24 (22.9%) 16 (44.4%) 87 (18.2%)

Stream 4 (1.2%) 3 (2.9%) - 7 (1.5%)

River 22 (6.5%) 4 (3.8%) 2 (5.6%) 28 (5.9%)

Total 337 (100%) 105 (100%) 36 (100%) 478 (100%)

Frequency of watering

Free access 47 (14.0%) 20 (19.0%) 11 (30.5%) 78 (16.4%)

Four times a day 6 (1.8%) 5 (4.8%) 1 (2.8%) 12 (2.5%)

Three times a day 43 (12.8%) 17 (16.2%) 6 (16.7%) 66 (13.8%)

Two times a day 157 (46.7%) 43 (40.9%) 18 (50%) 218 (45.7%)

One times a day 83 (24.7%) 20 (19.1%) - 103 (21.6%)

Total 336 (100%) 105 (100%) 36 (100%) 477 (100%)

Calf feeding Table 21 presents the two types of milk feeding practices to calf as reported from the

surveyed dairy farms. The majority of the dairy farms practiced bottle-feeding (76.7%)

and the remaining allowed suckling from dam (23.3%). Bottle feeding entails feeding

either with bulk milk, pooled from two or more lactating cows, or with dam milk.

Bottle feeding with dam milk dominated among the three farm sizes (>50%), while

bottle feeding with bulk milk tended to be more common in medium and large farms

as compared to small. Instead, suckling was more common on small dairy farms

(26.4%) as compared to the other two farm size categories.

38

Table 21: Methods of feeding the calves with milk

Feeding calves Herd size Total

Small Medium Large

Bottle feeding with bulk milk 65(19.3%) 27(25.5%) 13(35.1%) 105(21.9%)

Bottle feeding with dam milk 183(54.3%) 60(56.6%) 20(54.1%) 263(54.8%)

Suckling 89(26.4%) 19(17.9%) 4(10.8%) 112(23.3%)

Total 337(100%) 106(100%) 37(100%) 480(100%)

Feeding of calves with bulk milk could be one of the risk factors for major zoonotic

milk-borne disease transmission, including bovine tuberculosis. The prevalence of

bovine tuberculosis was significantly higher (LR chi(2)=8.262, p=0.016) in farms

practicing feeding of calves with bulk milk than in farms that were feeding with dam

milk or that allowed the calf to suckle (Table 22). This was likely because untreated,

unpasteurized bulk milk is prone to contamination with different pathogens when milk

from different animals are pooled as bulk milk. Unless bulk milk is heat-treated or

pasteurized, milk borne pathogens could easily be transmitted when it is fed to calves

(Kaske et al, 2012). If the dam of a calf is known to be infected with an infectious and

contagious disease, the calf should not be allowed to drink her milk, but should be

given milk from healthy cows.

Table 22: Distribution of dairy farms by bTB status and calve milk-feeding practices

Method of feeding calves Statistic Herd level bTB status

Total Likelihood ratio Ch2

negative positive

Bucket/bottle feeding from bulk milk,

No. of farms 26 49 75

8.262**

Percent 34.7 65.3 100.0

Bucket/bottle feeding from dam milk,

No. of farms 64 111 175

Percent 36.6 63.4 100.0

Suckling No. of farms 27 19 46

Percent 58.7 41.3 100.0

Total No. of farms 117 179 296

Percent 39.5 60.5 100.0

The dairy farm owners purchased different types and quantities of feed in the study

areas (Table 23). The most common feed types purchased in each dairy farm were, in

order: hay, oil cake (fagulo), molasses, mineral lick, wheat bran, crop residues and

brewery by-products. Mineral lick was purchased in the lowest average quantity. In

terms of average cost on individual feed products, farmers spent most money on cake

and hay because of the relatively large volumes consumed and high unit price.

Relatively better availability, longer shelf life, and relatively lower cost of hay as

compared to concentrates could explain why hay represented the largest share of feed

given to dairy cattle on the surveyed farms.

39

Table 23: Types and amount of animal feed purchased per year in the study dairy farms

Feed type Observation Average quantity

purchased (kg)

Average unit price

(birr)

Average total cost (birr)

Molasses 480 16,657.63 3.36 55969.64

Bran 480 9,966.46 5.47 54516.54

Cake 473 18,041.74 4.53 81729.08

Hay 475 21,316.92 3.38 72051.19

Crop residue 475 15,522.76 2.27 35,236.67

Brewery bi-product 475 11,302.47 2.61 29499.45

Mineral lick 475 420.85 5.41 2276.80

Salt 423 1,090.33 5.85 6378.43

Legume grains 475 3,531.939 5.67 20026.11

Formulated ration 391 7,645.48 5.16 39450.68

Total 397,134.6

In summary, feeding, and watering are crucial in the dairy sector to maximize milk

production and productivity, particularly in intensive management systems. „Free

access‟ feeding was more frequent among large farms than at the other farm categories

but the most common feeding pattern across all farms categories was to feed 2-3 times

a day. The most commonly used concentrate feed in the study dairy farms were

molasses, cake, brewery by-products, formulation rations and wheat bran, and they

spent most money on hay and concentrate feed. The majority of the surveyed dairy

farms practiced bottle-feeding of calves from dam and bulk milk, but some of them

allowed suckling from dam. Suckling was more common in small farms than in

medium and large, while in large farms it was more common that they fed their calves

with bulk milk. Calves from those farms are managed in calf pens with-in the same

farm and feeding milk from individual cow‟s milk or bulk milk with buckets after

milking.

These practices can have an implication on cattle-to-cattle transmission of contagious

diseases such as tuberculosis, brucellosis, anthrax, foot and mouth disease (FMD) and

Lumpy Skin Disease (LSD) as transmission can occur during feeding and watering

with common troughs (Thrusfield, 2005), bottle feeding from bulk milk and suckling

are also conducive for milk-borne disease transmission (Kaske et al, 2012) like

brucellosis, tuberculosis. Awareness creation is therefore critical to the dairy

producers about feed, milk- and water-borne diseases.

Farm bio-security Bio-security can be described as all practices and measures that can be adopted to

prevent or mitigate the risk of introducing infectious diseases in the dairy herd with

the associated health, welfare and economic consequences (Dorea et al., 2010).

Infectious diseases may be transmitted within and between farms by various routes,

such as through movement of infected live animals, trucks and other vehicles, people,

aerosols, fomites, wildlife, insect vectors, and animal products (Mee et al., 2010).

40

According to the respondents and physical observation the majority of the study dairy

farms were constructed with complete enclosure (70.6%) and the remaining 29.6% of

the farms had partial or no enclosure (Table 24). Farms without enclosures are

potential sources of disease transmission from one farm to another through e.g. contact

between animals, people, wildlife and vehicles.

The majority (74.8%) of the dairy farms used AI services and the remaining farms

used either borrowed bull (7.9%) or own bull (17.3%). However, the relative number

of calves produced by AI and by natural mating using bull was not explored in this

survey. Different infectious diseases caused by bacteria and virus could be transmitted

through natural mating and during AI unless the semen are free of those diseases. As

the name suggests, artificial insemination is a technique in which sperm is collected

from the male/bulls, processed, stored and manually introduced into the female

reproductive tract at appropriate time for the purpose of conception. AI has become

one of the most imperative techniques for the genetic improvement of farm animals

since preferable semen from genetically superior sires/males can be provided

relatively easy. There is a risk that venereal diseases of cattle like brucellosis, listeria,

vibriosis, bovine virus diarrhea, infectious bovine rhinotracheitis, and sometimes

bovine tuberculosis can be transmitted during natural mating with infected bulls or

through AI, unless the semen is collected from disease free bulls following standard

protocols (Wentink et al., 2000). So the use of a bull with no clear information about

its health status can be risky in terms of infectious disease transmission.

Nearly 81% of the surveyed dairy farms excluded the possibility of direct contact

between their cattle and dairy cattle from other farms. However, 68% of the dairy

farms shared their veterinarian with other farms and 59% allowed companion animals

(pets) to enter the farm, behaviors that could act as risk factors for transmission of

tuberculosis. About 9% of the hired workers at the studied farms had their own farm

and, as they had contact with the dairy animals, these workers could be vehicles of

disease transmission between the two farms.

Farm enclosure and tuberculin test reactivity are found to be significantly correlated at

p<0.05 (Table 25). Dairy cattle kept in fully closed dairy farms had on average higher

rate of tuberculin positivity than dairy cattle kept at partially enclosed farms. Dairy

farms, which were said to have contacts with different domestic and wild animals,

were more likely to be tuberculin positive. Tuberculin test result significantly varied

between dairy farms in contact with other animals at p<0.05.

41

Table 24: Farm bio-security measures

Table 25: Impact of farm enclosure for tuberculosis positivity

Bovine tuberculosis test results

farm enclosure Cramer’v

complete enclosure

partial enclosure

total

Negative 72 45 117

0.113*** Positive 267 96 363

Total 339 141 480 ***Significant at 1% level

Disease management

Common diseases The most common dairy cattle diseases, as identified by respondents of the study

areas, included mastitis, foot and mouth (FMD, lumpy skin diseases (LSD),

brucellosis, anthrax, blackleg, pasteurolosis, infertility, tick, bovine tuberculosis and

Farm biosecurity measures Frequency Percent

Farm enclosure

Complete 339 70.6

Partial 139 29

Not fenced at all 2 0.4

Reproduction

AI 359 74.8

Own bull 83 17.3

Borrowed bull 38 7.9

Frequency of using borrowed bull if yes

Every time 31 81.58

During AI shortage 7 18.42

Knowledge of bTB about the bull

YES 7 18.42

NO 31 81.4

Share vets

Yes 326 67.92

No 154 32.08

Contact with wildlife

Yes 44 9.17

No 436 90.83

Contact with companion animals

Yes 285 59.38

No 195 40.63

Contact with neighboring herds

Share pasture 20 4.17

Share water 4 0.83

Share both pasture and water 15 3.13

Direct contact 33 6.88

No contact at all 388 80.83

Indirect contact through boundaries

20 4.17

Animal ownership of Employees

Yes 44 9.2

No 436 90.8

42

leech in the order of severity and economic importance (Table 26). Based on results of

simple ranking. Mastitis was found to be the number one economically important

disease followed by FMD and LSD, in terms of both total and most important ranks.

Bovine tuberculosis, which is the disease of focus in the ETHICOBOTS project,

ranked at the edge of the diseases having more importance only than leech.

Table 26: Ranking of cattle diseases by farmers based on economic importance in their farms (n=479)

Disease N Rank Total score Overall rank

0 1 2 3 4 5

Mastitis 479 188 184 61 30 8 8 291 (60.8) 1st

Foot and Mouth disease 450 259 76 48 19 14 34 191 (42.4) 2nd

Lump skin diseases 479 293 49 31 30 25 51 186 (38.8) 3rd

Brucellosis 479 329 54 35 32 20 9 151 (31.5) 4th

Anthrax (aba senga) 479 368 47 3 6 3 52 111 (23.2) 5th

Blackleg (aba gorba0 479 397 42 73 3 30 82 (17.1) 6th

Pasteurolosis 479 398 20 16 25 12 8 81 (16.9) 7th

Infertility 479 412 24 14 19 6 4 67 (14.0) 8th

Tick infestation 479 419 12 20 11 9 8 60 (12.5) 9th

Bovine TB 479 444 16 3 10 3 3 35 (7.3) 10th

Leech 479 472 1 4 2 7 (1.5) 11th *Note: 0 no impact, 1 most important, 5 least important; items in braces are percentages from total farms

Mastitis can cause huge economic impact because if the dairy cattle are infected with

clinical mastitis there will be no milk at all and the quality of milk gets deteriorated

and not safe for human consumption. There is no complete cure to the infected teat

and full or partial teat blindness could result in complete or partial loss of milk

because of drug resistance development on mastitis causing pathogens. Poor hygiene

of the dairy animals, dairy farm workers and the farm as a whole can also contribute to

the mastitis severity. The disease is contagious to other dairy cattle and involves high

treatment cost.

Foot and mouth disease is highly contagious and morbid disease causing huge milk

loss of infected dairy cattle. If one animal in a herd is infected it could easily result in

infecting the whole cattle herd through contacts and sharing of common trough and

shade.

Abortion Abortion is defined as fetal death and expulsion between 42 (an estimated time of

attachment) and 260 days (the age at which a fetus is capable of surviving outside the

uterus) of gestation except fetal maceration and mummification. Abortion is a

manifestation and symptom of different cattle diseases (Peter, 2000).

About 28% of the sampled dairy farms reported abortion as a problem in their farms.

It occurred once in a year in about 18% of all farms whereas it occurred more than

once a year in about 10% of all farms during the survey period (Table 27). However,

there was statistically significant difference across the three herd sizes (F=21.08

p=0.000) indicating an increasing trend of observed abortion as we move from small

43

to medium and large herd farms. Further decomposition of the result by post-hoc test

also confirmed that abortion has been higher among higher herd size groups.

Table 27: Frequency of abortion in the dairy farm during the last 12 months

Abortion in a particular year

Small-herd

Medium-herd

Large-herd

Total F

0 226 55 14 295 (72.5)

21.08***

1 49 14 8 71 (17.4)

2 9 6 2 17 (4.2)

3 7 5 2 14 (3.4)

4 and above 1 3 6 10 (2.5)

Total 292 83 32 407 (100.0) Numbers shown in parentheses are percentages

With regard to time of abortion by the pregnant cows, more than half (54%) of the

farms (n=116) reported that abortion occurred in the late pregnancy, 27% in the early

pregnancy, and 19% in both late and early pregnancy (Figure 14). Abortion of dairy

cattle can occur due to different reasons e.g. biological/chemical reasons and due to

mineral/vitamin deficiency and fever. brucellosis, trichomoniasis, listeriosis, vibriosis,

BVD, rhinotracheitis are the common infectious causes of abortion (Tulu et al 2018).

It is known that late pregnancy abortions are associated with brucellosis (Parthiban et

al., 2015) while early abortions could be caused by trichomoniasis, vibriosis and

listeriosis.

Figure 14: Frequency of abortion at early or late stage pregnancy.

The respondents reported that mastitis and viral diseases of cattle were common

problems at the sampled farms. Dairy producers tried to manage these cattle diseases

by preventing disease transmission to farm through vaccination of animal, isolation,

and quarantine of animal, disease control by veterinary treatment, use of traditional

medicine, and by selling and/or slaughtering when their cattle showed any clinical

signs or symptoms of disease. Many of the reported diseases can cause loss of milk

production or lead to animal mortality. Analysis of the results also clearly showed that

a number of farmers did not take measures on some diseases and abnormalities like

44

infertility, brucellosis, and tuberculosis. Therefore, training and access to appropriate

disease prevention and control mechanisms should be considered as a priority

intervention for improving the development of the dairy industry.

Mitigating cattle diseases The respondents were asked for disease mitigation mechanisms for 11 different

diseases. Vaccination was used for mitigation against four diseases (anthrax (83%),

FMD (80%), LSD (78%) and blackleg (74%)) while hygiene, which is relatively less

costly, was used as a mechanism to reduce risk of mastitis and tick infestation (Table

28). However, few farms practiced segregation as a mitigation mechanism and for

leech, infertility, brucellosis, bTB, and pasteurolosis, the majority of farms did do

nothing to mitigate disease risk. One interesting result is that some dairy farms used

isolation as a mitigation measure to control bovine TB. The figure is low (13%) but

high in comparison to how often isolation was used as a tool for mitigation of the

other diseases. The aforementioned practices corroborate with results of a study by the

OIE (2014) where vaccination, hygienic practice, quarantine, isolation, early

diagnosis, and culling were reported as the major cattle disease prevention methods.

Table 28: Mitigation mechanisms to common cattle diseases prevention

Disease N Mitigation measure (%)

Vaccination segregation Hygiene Doing nothing

Lump skin disease 212 78 4 3 15

Anthrax 161 83 1 0 16

Bovine TB 83 18 13 1 64

Brucellosis 185 20 1 4 71

Pasteurolosis 111 36 5 8 50

Mastitis 293 36 2 47 12

Infertility 103 13 7 1 76

Leech 58 0 0 9 88

Tick infestation 100 10 0 42 41

Blackleg 134 74 0 0 25

Foot and mouth diseases 209 80 1 4 14

Total 448 34 119 472

Rank 2nd 4th 3rd 1st

Disease control The results presented in Table 29 show that, considering the individual disease types,

around 50% of the total respondents performed no disease control. On average across

the listed diseases, 42% used veterinary treatment as coping mechanism, whereas very

few farmers practiced other mechanisms; however, cattle were frequently culled or

sold due to infertility and bTB.

45

Table 29: Sample dairy farms common livestock diseases prevention and control

Disease N Coping mechanisms (% respondents)

Vet treatment

Traditional treatment

segregation Self-treatment

Do nothing

Culling by slaughter

selling

Lump skin diseases 212 61 2 1 1 34 0 0

Anthrax 162 43 0 0 0 57 0 0

Bovine TB 83 13 0 8 0 65 4 7

Brucellosis 186 39 1 0 4 54 0 1

Pasteurolosis 111 51 0 0 2 46 0 2

Mastitis 293 88 1 0.3 1 9 0 0

Infertility 103 21 0 0 0 56 3 18

Leech 58 0 3 0 5 91 0 0

Tick 100 44 1 0 7 47 0 0

Blackleg 133 36 3 0 1 59 1 0

FMD 209 62 3 1 4 30 0 0

Total 458 14 10 25 548 8 28

Total % of all responses

41.6 1.3 0.9 2.3 49.8 0.7 2.5

Rank 2nd 5th 6th 4th 1st 7th 3rd

Treatment to external parasites The result shows that only about a quarter of the sampled farms (n=479) practiced

dipping of animals in dipping chemicals as a treatment of external parasites. They

practiced dipping at farm once (35%), twice (26%), three times (14%), or four or more

times (25%) per year (Table 30). The average cost of dipping per animal and farm was

approximately Birr 30 and Birr 945 per year, respectively.

Table 30: Frequency of dipping to prevent against

external parasites (N=116)

Dipping per year Frequency Percent

1 41 35

2 30 26

3 16 14

>=4 29 25

Total 116 100

Treatment cost Farmers consult veterinarians or treat their cattle by themselves with antibiotics to

improve their health status and control secondary bacterial complication without

confirmatory diagnosis. This includes treatment of the dairy cattle that are coughing or

showing weight loss, signs, and symptoms which are typical of bovine tuberculosis

but not limited to this. The cost of treatment of common cattle diseases are described

in Table 31. The result indicates that the average cost was as high as 1696

birr/year/farm (LSD), followed by FMD (1589), and tick (1201). However, the

standard deviation indicates that there is high variability in the treatment cost of these

diseases.

46

Table 31: Total cost incurred per farm/year for treatment of cattle diseases in birr/year

Disease N Mean SD.

Lump skin disease 59 1696 5391

Anthrax 8 953 1185

Bovine TB 4 945 1147

Brucellosis 49 685 793

Pasteurolosis 49 505 925

Mastitis 221 949 1272

Infertility 16 522 944

Leech 2 300 283

Tick 41 1201 4399

Black leg 5 915 1232

FMD 69 1589 2571

Prevention cost For prevention purpose, over a one-year period, highest cost was incurred for the

prevention of mastitis (on average birr 850) followed by brucellosis (on average birr

840) (Table 32). However, in terms of the number of farmers who enumerated the

different diseases associated with cost of prevention, many (96 of them) reported

FMD and Anthrax followed by lump skin disease (93 of them) and blackleg (71 of

them).

Table 32: Total cost incurred at farm for prevention of a disease

(birr/year/farm)

Disease N Mean SD

Lump skin disease 93 441 1327

Anthrax 96 183 226

Bovine TB 5 231 157

Brucellosis 5 840 1214

Pasteurolosis 14 280 509

Mastitis 26 850 1042

Infertility 4 184 90

Tick 10 292 345

Black leg 71 211 347

FMD 96 383 877

Farm sanitation and other actions on disease control From our physical observation during interview, the majority of the dairy farms were

constructed from corrugated iron sheet roof, cemented floor, and were equipped with

feed and water trough. Cleaning of the farm was practiced by using water only after

removing of the dungs from the floor to improve the hygienic condition of the farm.

Most of the dairy farmers accumulated the dung nearby the farm because of lack of

enough space for dung removal from the barn areas, which could cause the risk of

occurrence of disease outbreak.

47

Based on the survey result (Table 33) about 76% of the dairy farms had latrine for the

sanitation of farm workers whereas the remaining 24% of the dairy farms had no

latrine. The dairy farm owners used different measures to control the disease after the

herd was tested positive. About 29% of the farms practiced selling, 22.2% performed

segregation, and 13.3% sent reactors for slaughtering, whereas 35.6% did not take any

measure. Controlling the disease in own herd by selling particularly tuberculin test

positive dairy cattle could, however, contribute to spreading tuberculosis. Taking no

measure was also one of the bad practices, which could facilitate the spread of bTB

within a herd and the risk of infection to dairy farm workers through air borne

transmission in direct animal contacts and through consumption of animal products.

Table 33: Dairy farmers practices used for control of bovine tuberculosis

and use of latrine

Characteristics Frequency Percent

Use of latrine at farm

Yes 365 76

No 115 24

Measures taken on bTB positive cattle

Slaughtered 6 13.3

Sold 13 28.9

Segregated 10 22.2

No action 16 35.6

The majority of the studied dairy farms applied bio-security measures like farm

enclosure, sanitation, and prevention of direct contact with other dairy cattle and

animals. A significant number of dairy farms tested positive for bovine tuberculosis,

sold their reactors, or kept them in their herd, behavior that could promote further

transmission of bovine tuberculosis. Therefore, appropriate policy on animal

movement restriction could limit disease transmission. In addition, application of

existing bio-security measures in all farms would likely reduce and prevent the spread

of animal diseases between farms, animals, and humans due to reduced interactions.

To improve the level of biosecurity, training is needed for dairy producers on how to

control transmission of bovine tuberculosis within and between dairy herds, which

will also reduce risk of zoonotic transmission to humans.

Milk production and processing

Production Urban and peri-urban dairy cattle production has been developed in response to the

fast-growing demand for milk and milk products. Dairy cattle kept in this type of

production system are mainly cross breeds specifically targeting consumer in the

nearby town and city. On average, each dairy farm produced 90 liters of milk per day.

The total amount of milk produced per day in the study dairy farms was 43,250 liters

(Table 30). From this volume, 80% was sold and the remaining 20% was used for calf

and household consumption. The mean price of a liter of milk in the study dairy farms

ranged from 14 to 16 Eth birr wherever they sold it. The price of milk was a little bit

more expensive when sold from small and medium size dairy farms than from large

farms. The amount of milk consumed at the dairy farms was 7.7 liters per day in

48

medium farms and 2.9 liters per day in small size farms. The amount of milk

consumed positively depends on the number of the household members and dairy farm

workers at the farm.

The milk yield per day per cow were 3.7, 11, and 13.2 liters in indigenous, cross, and

high-blood cows, respectively (Table 34) and the results also shows that the milk yield

per cow generally increased with farm size. Records from 540 lactating cattle were

collected. The milk yield is above the national average of 1.54 liters/cow/day (Tefera

et al 2010), that also included cattle in the extensive husbandry systems.

Table 34: Average milk yield (liters) per day per cow of different breeds and herd sizes

Herd size Average milk yield per day per lactating cow

Indigenous cows Crossbred cows High grade cows

Small-herd (3-19) farm N 31 296 52

Sum (liters) 107.6 3168.1 645.9

Mean 3.5 (1.5-8.0) 10.7 (4.0-23.0) 12.4 (5.6-19.0)


N 8 85 25

Sum (liters) 26.5 966.7 363.1

Mean 3.3 (2.0-6.0) 11.4 (5.0-25.0) 14.5 (10.0-20.0)

Large-herd (>49) farm N 5 33 5

Sum (liters) 27.0 403.1 73.0

Mean 5.4 (5.0-6.0) 12.2 (3.40-19.0) 14.6 (8.0-26.0)

Total N 44 414 82

Sum (liters) 161.1 4537.9 1082.0

Mean 3.7 (1.5-8.0) 11 (3.40-25.0) 13.2 (5.6-26.0)

Dairy cows kept in the study dairy farms are described in Table 35. Based on the

survey result 4861 dairy cows were kept in the study dairy farms for the purpose of

milk production excluding calves, heifers, bulls and oxen. From the total (4861) dairy

cows kept in the surveyed dairy farms, 4258 (87.6%) were for milking. About 97.9%

of lactating dairy cows were of exotic and crossbreed cattle and the remaining 2.1%

were cows of local zebu breeds. Regardless of the breed, about 60.7% of the dairy

cows were found in large and medium dairy farms and the remaining 39.3% of them

in small size farms, while a higher proportion (59/90, 65.6%) of zebu cows were

found in small holder dairy farms (Table 35).

Table 35: Number of lactating cows (the last 12 months) stratified on herd size

Herd size No. of indigenous cows

No. of crossbred cows

No of exotic (high grade crosses) cows

Total

Small (3-19) 59 1402 214 1675 (39.3%)

Medium (20-49) 15 976 297 1288 (30.3)

Large (>49) 16 1142 137 1295 (30.4%)

Total 90 (2.1%) 3520 (82.7%) 648 (15.2%) 4258

Average milking days The average milking days of indigenous, cross, and high grade cross bred cows were

221, 275 and 279 days, respectively (Table 36). The length of the lactating period was

49

slightly longer in cross and exotic breeds than in local breeds. Interestingly, the

numbers of lactating days of local breed dairy cows kept in large farms were more

than those kept in medium and small farms. This could be due to the possibility of

better management in the latter farm system.

Table 36: Average days of lactating for different breeds of dairy cows

Herd size Indigenous milking cows

Cross breed milking cows

Exotic milking cows

Smallholder (3-19) 218 274 276

Medium-herd (20-49) 203 277 285

Large-herd (>49) 271 282 270

Total 221 275 279

Per capita milk consumption The average per capita milk consumption per day for our sample was found to be 0.25

liters with std. dev. of 0.26. We reached at this figure asking farmers for consumption

on a daily and monthly basis and not on yearly basis, as that would be more difficult to

recall and estimate, especially as there are many fasting days in the calendar of the

Ethiopian orthodox church whose members don't consume milk during fasting days

(in this sample the orthodox Christians made up 83.26%). This average per capita milk

consumption figure is quite high and statistically significant (t=16.09; P=0.000) as

compared to the national average of 19 liters per year (0.05-0.10 liters per day). This is

neither surprising nor representative of the general population as we surveyed dairy

farmers who have better access to milk and better habit of milk consumption than the

public. The mean difference in per capita milk consumption per day was not found to

be statistically significant between sexes, religions, education status, as well as regions

(Table 37).

Table 37: Per capita milk consumption per day (in liters) by different socioeconomic variables

Socioeconomic variables Particulars N mean SD t/F value

Respondent's sex Female 113 0.2224 0.1615 -1.1503

Male 366 0.2553 0.2819

Region Addis Ababa 164 0.2741 0.3326

1.61

Oromiya 136 0.2535 0.2116

Amhara 58 0.1987 0.2266

Tigray 59 0.1965 0.1385

SNNPR 25 0.2698 0.1740

Education Illiterate 34 0.1949 0.2077 -1.2327

Literate 446 0.2516 0.2611

Religion Muslim 20 .1823 .1614 -1.0449

Christian 458 .2488 .2603

Only 1.53% of the respondents indicated that their main source of milk is pasteurized

milk. Pasteurized milk was mentioned as second rank by 7.63% respondents, 14.77 %

as third rank and by 18.75% of the respondents as the fourth rank.

50

Milk processing Across the surveyed farms, milk was processed mainly into cheese (41.7%), butter

(33.5%), and yoghurt (21.9%) (Table 38). However, butter was processed by large

farms in larger proportion compared to the other farm categories. The average price of

butter and cheese per kg were 150 and 60 Eth birr, respectively, which could be one of

the driving force for the processing of milk in to different by products.

Table 38: Processing of milk in to different milk products by farm size (in percent)

Milk product Small size farm(n=254)

Medium size farm (n=96)

Large size farm (n=29)

Total (n=379)

Butter 33.1 32.3 41.4 33.5

Cheese 41.3 43.8 37.9 41.7

Yoghurt 24.0 19.8 10.3 21.9

“Arera” 1.6 4.2 10.3 2.9

Total 100 100 100 100

Price of processed milk Unit price of processed milk products were described in Table 39. The average unite

price of processed milk products per kg was ranged from 51 to 125 birr. The average

price of butter for consumption and butter for cosmetics were 125 and 105 birr

respectively. On average, 15- 20 liters of milk is needed to produce one kg of butter, 6

kg of cheese, and 10 liters of butter milk. Therefore, the average price of 1kg of butter

was lower than the price of raw milk used to produce one kg of butter with an average

price of 15 birr per one litter of milk. Therefore, if the market is available selling of

raw milk is better than selling of processed milk products.

Table 39: Price of milk products per

kg from processed milk

Product Mean

Butter for consumption 125

Cheese 63

Yoghurt 51

Butter milk 51

Butter for cosmetics 105

Milk can clean Milk is perishable food item that requires clean equipment, preservatives, and low

storage temperature during milk handling and transportation. Water, detergents, and

smoking were tools used in the study dairy farms for cleaning of milk cans (Table 40).

The main practice involved cleaning the milk cans by washing with water and

detergent (performed by 65.2%) while smoking after washing was added as a cleaning

step by many (34.8%). Farmers used locally purchased detergents for washing milking

cans.

51

Table 40: Methods for milk can cleaning

Method Frequency percent

Wash with water and detergents 302 65.2

Wash with water, detergents and smoking 161 34.8

Total 463 100.0

Actors and milk price in the milk chain Produced milk is sold to consumers, private traders, cooperatives, processors and

collectors (Table 41). The majority of the producers said that they sold milk mainly to

consumers (70.2%), followed by sales to hotels (27.9%) and to collectors (23.5%), but

milk was also sold to several other actors in the milk chain. Price of milk per liter was

higher when sold to consumers and hotels.

Table 41: Proportion of milk sold to different actors and average price of milk in

each actor

Actors of milk market Proportion

(%) Average price/liter

(birr)

Consumers 70.2 16.5

Collectors 23.5 13.6

Small scale processors 7.7 13.9

Trader/wholesalers 4.6 11.7

Retailers 8.1 13.8

Cooperatives 3.3 10.5

Medium and large scale processors 6.9 12.7

Hotels 27.9 15.6

Processing of milk by the producers indicated that there is raw milk market problem in

urban and peri-urban production system particularly during fasting time because of the

low demand of raw milk by consumers. During this time, the price of milk drops even

to zero for most of the producers and the milk producers then tend to instead process

the milk into different products such as butter and cheese as a risk mitigation strategy.

Improvement of market linkage between seller and buyer is needed to help producers

sell fresh milk either directly (raw) or packed (processed). In addition, enforcement of

quality standards is required to increase the credibility of other sellers.

Sources of dairy farm income other than milk Farm owners generate or receive their income from various sources in addition to the

sale of milk (Figure 15). The main sources of income, other than milk sales, that were

reported by the respondents include livestock sales (37% of the farms), house rentals

(34% of the farms), regular employment salary (31% of the farms), and petty trade

(26% of the farms). A significant number of respondents received income in form of

pension (20% of the farms). The distributions of dairy farmers across the remaining

income sources were insignificant. These results indicate that farmers diversified their

sources of income and that a significant proportion of them were not fulltime dairy

farmers. Our data also showed that 40.5% of the respondents from large farms earned

income from cattle sale while the corresponding figures for small and medium farms

were only 21.9% and 34.9%, respectively. This difference was statistically significant

52

at 1% level (chi2=11.2) which could indicate that the large farms served as sources of

replacement stock for other dairy farms as well as sources of feedlot cattle.

Figure 15: Distribution of dairy farm owners by sources of income

Sources of income on dairy farms, besides those derived from the sale of milk, could

be defined in terms of the amount of money generated from occasional practices

(observed mainly among large farms) such as cattle sales and from less investment-

generating means of living (such as salaries, pension and petty trade) implying that

dairy business is not the only source of income.

Bovine tuberculosis One of the main aims of the ETHCOBOTS project is to improve understanding of the

impact and risk factors of bovine TB in the Ethiopian dairy sector. This socio-

economic survey therefore set out to explore possible associations between bovine TB

prevalence and different factors in the surveyed dairy farms.

Herd level bTB prevalence Results of bTB testing of 475 farms in four study sites revealed that on average 46.4%

of the farms were bTB positive; i.e., in a bTB positive herd at least one cattle were

diagnosed as bTB positive using the standard PPD test. The highest bTB incidence

was observed in Addis Ababa with 63.3% of the sampled farms being bTB positive

and the lowest rate was observed in Hawassa with 11.1% bTB positive herds (Table

42). Statistically significant difference was observed between test location and bTB

herd level prevalence rate (LR-chi square=82.039 and P=0.000).

53

Table 42: Herd level bTB prevalence rate by study site

Study site Statistics Herd level bTB status Total LR Chi2

Negative Positive

Addis Ababa No. of farms 58 100 158

82.039***

Percent 36.7 63.3 100.0

Oromia towns surrounding Addis Ababa

No. of farms 59 78 137

Percent 43.1 56.9 100.0

Gondar No. of farms 54 11 65

Percent 83.1 16.9 100.0

MekelleMekelle No. of farms 37 24 61

Percent 60.7 39.3 100.0

Hawassa No. of farms 48 6 54

Percent 88.9 11.1 100.0

All study sites No. of farms 256 219 475

Percent 53.9 46.1 100.0

Farm ownership and bTB status Investigation into the relationship between farm ownership and bTB status showed

that there was a statistically significant association (Fisher's Exact value= 5.286;

P=0.063). The privately-owned farms have lower bTB herd prevalence proportion

(43.9%) as compared to the government owned (66.7%) and cooperative owned

(54.7.%) farms. However, this relationship disappears when we control for farm size

and the result shows that the above relationship holds true only for the smallholder

farms (Table 43). That is, of the 171 privately owned small farms 45.6% were bTB

positive, while out of the 28 government and cooperative owned small farms 71.42%

were found to be bTB positive (Fisher's Exact Value of 7.07 and P value of 0.017).

This may be explained by privately owned farms being better in farm management and

follow up, including disease control and cattle movement, as compared to public and

communal ownership of the farms.

54

Table 43: Relationship between farm ownership and farm bTB status

Farm ownership (all farms) Herd bTB status Total Fisher's Exact Farm ownership (small farms) Herd bTB status Total Fisher's Exact

Negative Positive Negative Positive

Private Count 215 172 387

5.286*

Private Count 177 99 276

7.07**

% 55.6 44.4 100.0 Percent 64.1 35.9 100.0

Government Count 5 10 15

Govern-ment Count 2 3 5

% 33.3 66.7 100.0 Percent 40.0 60.0 100.0

Cooperative Count 30 34 64

Cooper-ative Count 25 20 45

% 46.9 53.1 100.0 Percent 55.6 44.4 100.0



% 53.6 46.4 100.0 Percent 62.6 37.4 100.0

55

Farm herd size and bTB status Assessment of the association between farm herd size category and bTB status shows

that bTB herd prevalence was found to be 46.17% of the sampled farms (Table 44).

The association was statistically significant at 1% level (LR Chi (2) = 38.43),

indicating that bTB status and farm herd size are interdependent. Thus, it is inferred

that percentage of bTB herd prevalence increases with increase in farm herd size. Out

of the large farms, 75.6% were bTB positive while the medium and small farms had

64.1% and 37.0% herd positivity, respectively.

Table 44: Herd level bTB prevalence status (%) versus herd size

Herd size Negative herds bTB positive herds Likelihood ratio Chi square Count Percent Count Percent

Small (3-19) 206 63.00 121 37.00

38.43*** Medium (20-49) 38 35.85 68 64.15

Large (>49) 9 24.32 28 75.68

All tested herds 253 53.83 217 46.17

Likewise, the animal level prevalence rate was also dependent on farm herd size.

Analysis of variance showed significant difference in animal level bTB prevalence

rate among the three farm herd sizes (F=13 and P=0.000). The small farms have

average animal level prevalence rate of 12.1% (SD=22.7) while the medium and large

farms have average animal level bTB prevalence rate of 22.7% (SD=22.4) and the

large farms have average animal prevalence rate of 28.6% (SD=29.9); however, the

mean difference in farm bTB prevalence rate between the bTB positive medium and

large farms was not found to be statistically significant.

Farm bTB history We investigated the bTB test history of these farms and found out that 159 out of the

480 sampled farms (33.1%) had been bTB tested in the past and 45 (28.3%) of these

farms were then bTB positive. However, 26.7% of these previously bTB positive

farms were found to be bTB negative in our survey, indicating that they had

eliminated the bTB positive animals after the previous test either by culling (e.g. by

sales to slaughter houses) or by sells to other dairy farms. The latter is possible in the

absence of a strong surveillance and animal movement control mechanism and

increases the risk of bTB transmission in the country.

Multivariate analysis of risk factors for bTB incidence We used a binary logistic regression model to analyze the risk factors involved in bTB

incidence at farm level. The dependent variable, herd level bTB incidence, was

measured as a dummy variable where 1 stands for bTB positive herd and 0 stands for

bTB negative herd. Seventeen variables, which are related to location, animal

husbandry, bio-security, access to veterinary services, farm herd size, farm area size,

labor intensity level, and access to zoonosis extension, were entered as independent

factors determining the odds of being bTB positive herd. The results revealed that our

model was robust with log likelihood ratio chi square of 148.38 and p- value of 0.000

56

that indicates that the model is significantly different from the intercept only model in

determining the outcome variable, herd level bTB status. Hosmer and Lemshaw test of

goodness of fit was found to be non-significant, indicating that the model is fit. Of the

variables in the equation, location, possibility of contact with neighboring livestock,

farm private ownership, the use of AI as a main breeding method, wildlife access to

the farm, farm herd size and total farm area were found to have significant effect on

the odds of being bTB positive farm (Table 45).

With regard to location, farms in Addis Ababa city had the highest bTB prevalence

(Table 45). With this as the baseline, farms located in Gondar, Hawassa, and Mekelle

showed significant decrease in odds of being bTB positive by factors of 0.12, 0.09,

and 0.31, respectively, as compared to Addis Ababa. The odds of dairy farm being

bTB positive in Oromia towns surrounding Addis Ababa was however not significant

when compared to Addis Ababa.

Among factors related to animal husbandry, we entered feeding, watering, grazing and

breeding related variables. However, none of them were found to be statistically

significant in determining the odds of the bTB incidence at herd level. However,

Ameni et al. (2006) demonstrated that animal husbandry conditions can have major

influence on bTB prevalence. According to that study, the prevalence and severity of

tuberculosis lesion were higher in dairy cattle managed with indoor feeding as

compared to grazing on pasture. In the current study though, the vast majority of

investigated farms reared their cattle under zero grazing conditions (81%, Table 17) or

partial grazing (19%; Table 17), it is difficult to make a similar comparison as

performed by Ameni et al. Moreover, as they stated, housing predisposes cattle to TB;

the closer animals are packed together, the greater the chance that TB will be

transmitted. Also, apart from physical factors like close contact, it is also possible that

stress caused by overcrowding or nutritional differences between housed and pastured

animals contributed to higher disease prevalence (Ameni et al., 2006).

57

Table 45: Logistic regression results of risk factors for herd level bTB incidence

Variable B S.E. Wald df Sig. Exp(B) 90% C.I. for EXP(B)

Lower Upper

Location (in comparison to Addis Ababa)

33.606 4 .000

Oromia towns near Addis Ababa -.331 .343 .934 1 .334 .718 .408 1.262

Gondar -2.157 .485 19.795 1 .000 .116 .052 .257

Mekelle -1.183 .414 8.163 1 .004 .306 .155 .605

Hawassa -2.422 .567 18.272 1 .000 .089 .035 .225

Husbandry

Separate watering trough (yes) -.309 .350 .780 1 .377 .734 .413 1.305

Tap water (Yes) .483 .327 2.181 1 .140 1.621 .947 2.777

Separate feeding trough (Yes) .145 .308 .221 1 .638 1.156 .697 1.917

Zero grazing (Yes) .436 .383 1.295 1 .255 1.547 .823 2.905

Bulk milk (Yes) -.314 .310 1.029 1 .310 .730 .439 1.216

Breeding by using AI (Yes) -.451 .305 2.189 1 .139 .637 .386 1.052

Bio-security

Cattle contact with neighboring farm (No) -.635 .376 2.857 1 .091 .530 .286 .983

Wildlife access (Yes) 1.298 .476 7.421 1 .006 3.661 1.672 8.016

Complete enclosure (Yes) .032 .301 .011 1 .915 1.033 .629 1.695

Private vet (Yes) -.092 .291 .099 1 .752 .912 .566 1.471

Share vet (Yes) .261 .297 .769 1 .381 1.298 .796 2.116

Farm size

Cattle managed per worker .032 .028 1.255 1 .263 1.032 .985 1.081

Number of cattle .048 .011 20.183 1 .000 1.049 1.031 1.068

Total farm area in meter square -.001 .000 4.052 1 .044 .999 .999 1.000

Other variables

Farm ownership (Private) -.617 .329 3.527 1 .060 .540 .314 .926

Zoonosis extension (Yes) -.717 .291 6.059 1 .014 .488 .303 .788

Constant -.332 .718 .214 1 .643 .717

With regard to bio-security related variables, bTB prevalence versus possibility of

contact with other livestock was found to be significant. Our result showed that the

odds of being bTB positive reduced by a factor of 0.53 if the farm had no possibility

of contact with other livestock in the neighborhood. This result is similar with findings

by Tschopp et al (2009) and Freddy et. al. (2009); the prevalence of bTB was higher in

dairy farms which kept other livestock species than farm which did not. Likewise,

possibility of giving wildlife access to farm was found to be statistically significant,

increasing the odds of being bTB positive by a factor of 3.66. Similar result was also

reported by Proaño-Perez. et al. (2009). These results imply that bio-security,

especially allowing contact between the herd and animals in neighboring herds and

wildlife is a major risk factor for bTB and any control strategy need to consider this.

In addition to the above-mentioned variables, farm herd size in head count and farm

area measured in meter squares were found to be statistically significant risk factors

for bTB incidence at herd level. Our results indicated that farm herd size is a major

risk factor that with a unit increase in head counts the odds of the farm being bTB

positive increases by a factor of 1.03. Admassu et al (2014); Ameni and Erkihun

(2007); Nuru et al. (2015) and Firdessa et al (2012) indicated the strong relationship

http://www.ajtmh.org/search?author1=Freddy+Proa%C3%B1o-Perez&sortspec=date&submit=Submit

58

between farm herd size and bTB infection in intensive dairy systems. In addition to

this, farm area in meter squares, which serves as a measure of confinement, was found

to have a negative and significant effect on the odds of bTB incidence at herd level;

i.e., with a unit increase in farm area, the odds of being bTB positive deceases by a

factor of 1.0. These results also imply that farm size, as measured by herd head count

and land area, are a major risk factor for bTB incidence at herd level.

Other variables such as farm ownership and extension service (on the topic of zoonotic

diseases control) were also hypothesized to be possible risk factors for herd bTB

incidence. The results of the model testified that, as hypothesized, private farm

ownership has a negative effect on the odds of being bTB positive by a factor of 0.54..

This might be because, as shown above (see Farm ownership and bTB status), public

or communal ownership of the farms might not have close follow up and proper

management of farms, including disease control and cattle movement, as compared to

privately owned farms. The effect of extension training in zoonosis was also found to

be important in reducing the odds of being bTB positive by a factor of 0.49 implying

that extension education on zoonosis might have an impact on bTB control.

In summary, our multivariate analysis of bTB risk factors revealed that location, bio-

security, farm size, farm ownership, and zoonosis extension were important risk

factors that need to be considered in any bTB control strategy. The fact that bio-

security measures were found to be important risk factors and the fact that zoonosis

extension had implications in bTB incidence suggest that training on risks of disease

transmission – within herd, between herd, and zoonotic transmission could go a long

way in controlling bTB epidemics in the Ethiopian intensive diary system around

urban and Peri-urban areas. Moreover, our result also indicated that dairy herd size is a

major strategic policy issue that needs to be considered in any bTB control strategy

design.

Knowledge about zoonosis Based on the result in this survey, 41.1% of the respondents replied that bovine

associated diseases could be transmitted from animals to humans via consumption of

raw meat and milk. According to them, the most important causes and symptoms of

zoonotic diseases include anthrax, brucellosis, tuberculosis, koso (tapeworm),

abdominal discomfort, and amoeba. They also perceived hypertension and uric acid as

zoonotic diseases. About 37% of the respondents perceived the transmission of animal

diseases to humans without differentiating the source of infection. Some 37.6 % of

dairy farmers mentioned tuberculosis is a major disease transmitted from animals to

humans through contact with infected animals and humans and by consumption of raw

meat and milk. About 13% of the respondents considered tuberculosis as zoonotic to

humans.

Most respondents (92.7%) were aware about tuberculosis in general. The dairy

farmers were asked about the experience of confirmed human tuberculosis cases in

their dairy farms during the last five years, the last three years, or more recently. The

responses indicated that confirmed tuberculosis cases had occurred in dairy farm

59

workers and the prevalence within the specified time periods are depicted in Table 46.

Accordingly, as many as 10.4% of the farms have had a confirmed tuberculosis case

among dairy farm workers in the last five years, their proportion decreased to 4.4% in

the last three years, while the corresponding cases in more recent years were 13.8%.

Only 20 respondents could remember type of TB disease, and both pulmonary and

extra-pulmonary TB were defined. As these were historical cases, any clinical details

around each case were not collected. Thereby, the likely disease agent

(Mycobacterium bovis or Mycobacterium tuberculosis) was not possible to identify

and the source of infection remain unknown for these cases. However, given that

many of these farm workers were likely exposed to bTB by direct interaction with

disease animals or by consumption of e.g. unprocessed milk, it is possible that reactor

dairy cattle could be the source of tuberculosis for some dairy farm workers of these

farms.

Table 46: Tuberculosis cases in dairy farm workers in the study areas in different period

TB cases reported by dairy farmers Frequency Percent

Any diagnosed TB cases at FARM in the last five years

Yes 50 10.4

No 429 89.6

Total 479 100

Diagnosed TB cases among farm workers in the past three years

Yes 21 4.4

No 457 95.6

Total 478 100

Recent diagnosed TB cases at farm

Yes 66 13.8

No 413 86.2

Total 479 100

Types of confirmed TB cases reported by farm workers

Pulmonary TB 17 73.9

Extra-pulmonary TB 3 13

Total 20 100

Bovine tuberculosis is highly prevalent in cattle in intensive dairy farms by the

zoonotic impact on those living or working on the farms is poorly understood. Based

on what the dairy farmers reported in our survey, confirmed TB cases in dairy farm

workers were found to be higher in farms diagnosed with bTB positive cattle. The

variation of the prevalence of confirmed tuberculosis cases among dairy farm workers

was statistically significant at p<0.005 (Table 47). Most of the dairy farm workers

were found in large dairy farms.

The knowledge of the respondents on TB signs and symptoms, source and ways of

transmission, and prevention methods are presented in Table 47. The most common

signs and symptoms typical of tuberculosis in humans were noticed by a majority of

the respondents. Those were weight loss (87.8% of respondents), cough that lasts

more than 3 weeks (85.5%), coughing (85.1%), fever (74.1%) and headache (73.5%).

Tuberculosis is one of the zoonotic diseases transmitted from animal to human and

vice-versa. The majority of dairy farmers were aware of human-to-human and animal-

60

to-animal transmission of tuberculosis. However, they have limited knowledge about

the zoonotic nature of bTB. When the respondents (dairy farmer owners or managers)

were asked about their knowledge and awareness level on human and bovine

tuberculosis separately, it was revealed that they had better knowledge and

understanding on human tuberculosis than on bovine tuberculosis.

Table 47. Awareness and knowledge of the respondents on TB in the study areas (N=478)

Signs and symptoms of TB on human No. of respondents who knew

Percent

Rash 193 40.4

Coughing 407 85.1

Cough that lasts more than 3 weeks 426 89.1

Coughing up blood 340 71.1

Severe head ache 366 76.6

Weight loss 437 91.4

Nausea 235 49.2

Fever 369 77.2

Fever without clear cause which lasts more than 7 days 298 62.3

Chest pain 282 59.0

Shortness of breath 327 68.4

Ongoing fatigue 263 55.0

Asthma 119 24.9

Raw meat and milk were considered as the major sources of tuberculosis infection by

30.3% and 33.3% of the farm owners, respectively (Table 48). Tuberculosis could be

transmitted from animal to human and human to human through different methods.

About 94% of the respondents replied that the disease could be transmitted through air

from infected people cough and sneeze and 70% from consumption of raw milk (Table

49). Major tuberculosis prevention methods given due attention by dairy farm workers

were, covering mouth and nose when coughing and sneezing (94.4%) and eating good

nutrition (68.5%) (Table 50). The result is in disagreement with Fikre et al (2014)

research report on cattle owners‟ awareness on tuberculosis in and around Mekelle,

that, vaccination, ventilation, personal hygiene, consumption of cooked and boiled milk were described as the major prevention methods of bovine tuberculosis.

Table 48: Knowledge on transmission cycle of BTB

Particulars No. of respondents who knew

Percent

Mechanism of transmission (N=473)

Animal to animal 377 79.7

Animal to human 352 74.4

Human to animal 161 34.0

Human to Human 450 95.1

Routes of bTB transmission from animal to human (N=498)

Raw meat consumption 151 30.3

Raw milk consumption 166 33.3

Respiratory route 75 15.1

Close contact with infected animals 17 3.4

I do not know 89 17.9

61

Table 49. Favorable factors for the transmission of TB (N=479)

Particulars No. of respondents who knew

Percent

Air borne from infected people coughing and sneezes 450 93.9

Through hand shake 120 25.1

Through sharing dishes 313 65.3

Eating from the same plate 331 69.1

Contact with inanimate objects 253 52.8

Exposure to wind and cold air 249 52

Drinking raw milk 338 70.6

Raw meat consumption 325 67.8

Contact with infected animal 277 57.8

Contact with TB lesions 200 41.8

Table 50. Prevention methods of TB transmission cycle

Particulars No. of respondents who know

Percent Total

Covering mouth and nose when coughing and sneezing 452 94.4 479

Avoiding hand shaking 124 25.9 479

Avoid sharing dishes 322 67.2 479

Eating good nutrition 328 68.5 479

Close windows at home 162 33.8 479

Avoid exposure to wind and cold air 270 56.4 479

Avoid drinking raw milk 338 70.6 479

Avoid raw meat consumption 328 68.5 479

Avoid contact with infected animal 288 60.1 479

By praying 168 35 479

Washing hands after contact with public items 320 66.8 479

Milk and meat consumption patterns and zoonotic risk

Raw milk consumption Farmers were asked about their habit of raw milk consumption (raw or uncooked and

unpasteurized). Most (77.45%) of the farmers have indicated that they never drank

raw milk and about 20.46% were frequent drinkers of raw milk with varying degrees

of frequency (Table 51 Only 8.14% (n=39) were regular drinkers of raw milk with a

frequency of once or more than once a day. Although the majority of the interviewed

farmers indicated that they do not drink raw milk, 81.84% of them do actually drink

fermented milk commonly called "ergo" in Amharic.

We investigated the relationship of sex, education, region, religion and age with raw

milk consumption frequency. We found out that raw milk frequency is not related to

any of these socioeconomic variables except with region (location). We found a

statistically significant systematic relation between region (location) and raw milk

consumption habit (LR chi2 (4) = 28.6986, P = 0.000); i.e., only 5% of farmers from

Mekelle indicated that that they consumed raw milk while 36.54% from Hawassa did

the same. This implies that rather than demographic variables such as sex and

education, difference in raw milk consumption by location can be explained by

cultural differences due to religion and location.

62

Table 51: Raw milk consumption frequency

Frequency n Percent Cumulative

More than once a day 10 2.09 2.09

Once a day 29 6.05 8.14

3-6 times a week 30 6.26 14.41

Once/twice a week 14 2.92 17.33

Once/twice a month 15 3.13 20.46

On Special occasions only 10 2.09 22.55

Not at all 371 77.45 100

Total 479 100

We also found out that neither general training on zoonosis transmission mechanisms

(Fisher's exact = 0.415) nor the specific training on bTB had any relation with raw

milk consumption frequency (Fisher's exact = 0.680). This might be because the

trainings were not adequate to cause behavioral change or it might be due to perceived

nutritional qualities, good taste, or health benefits as indicated by Oliver et al (2009).

Moreover, we did not see any difference in the frequency of raw milk consumption

between those farms whose animals had been tested for bTB previously and those that

had not, indicating that knowledge of bTB status of the farm had not brought about

any change in raw milk consumption behavior of farmers.

The farmers were also asked about their knowledge of the risk of drinking raw milk as

a possible disease transmission cause. The vast majority of the respondents (88.31%,

n=423) indicated that they know that drinking raw milk can cause disease; only 5.64%

(n=27) indicated that it does not cause any disease and 6.05% (n=29) indicated that

they do not know.

They were also asked how healthy do they think drinking raw milk is (Table 52). The

majority of them (77.7%) indicated that it is unhealthy or very much unhealthy.

Despite their knowledge of the possible risk of disease transmission a considerable

number of them (20.46%, n=98) consumed raw milk frequently (Table 51). This might

be related to the fact that although they may possibly be infected by the pathogen,

since the disease has a chronic nature without apparent clinical symptoms; they may

tend to ignore it and continue drinking raw milk.

Table 52: Perception of dairy farmers about how healthy drinking

raw milk was

Perception n Percent Cumulative

Very much healthy 23 4.8 4.8

Healthy 48 10.1 14.9

Do not know 35 7.4 22.3

Unhealthy 238 50.1 72.4

Very much unhealthy 131 27.6 100

Total 475 100

Out of the surveyed farms, which indicated that they had habit of raw milk

consumption, 46.67% had bTB positive animals in their herd and there was no

63

statistically significant difference in such habit between bTB positive and negative

farms. However, we found that there is a statistically significant relation between

„habit of raw milk consumption‟ and occurrence of „confirmed TB case on farm‟ in

the last three years (likelihood-ratio chi2 (1) = 12.0874, P = 0.001). Of those farm

households, which reported occurrence of TB in their farm in the last three years,

40.63% indicated that they are in the habit of raw milk consumption, while only

19.90% of those reported no TB case on farm had the habit of raw milk consumption.

This result warrants further clinical investigation of the farms and the cases.

About 81.84% of the respondent farmers indicated that they consume yoghurt

(fermented milk), also known as „ergo‟ in Amharic; only 18.16% indicated that they

never consume ergo. Other authors also found that ergo consumption is very high,

especially among adults (Tolosa et al, 2016; Duguma and Janssens, 2015).

Pasteurized milk consumption As show in Table 53, the majority of sampled farmers (88.94%) do not drink

pasteurized milk. Our data also showed that only 37.79% (n=181) of them knew the

benefits of pasteurization, 54.07% (n=259) did not know about its benefits, and 8.14%

(n=39) of them had never heard about pasteurization.

Table 53: Pasteurized milk consumption frequency

Consumption n Percent Cumulative

At least once a day 3 0.63 0.63

3-6 times a week 9 1.88 2.51

Once/twice a week 6 1.25 3.76

Once/twice a month 9 1.88 5.64

On special occasions only 26 5.43 11.06

Not at all 426 88.94 100

Total 479 100

Investigation of the relationship between education and pasteurized milk consumption

frequency showed no systematic relationship (Fisher's exact = 0.690). Since Addis

Ababa and its surrounding towns in central Ethiopia is where the majority of the large

farms and pasteurization plants have been established, we expected regional difference

in consumption frequency of pasteurized milk but no relationship was found between

region (study site) and pasteurization (Fisher's exact = 0.480). Similarly, there was no

statistically significant systematic relation between sex and frequency of pasteurized

milk consumption (Fisher's exact = 0.156). The in general low level of consumption of

pasteurized milk among farmers could be because most of them they have higher

access to unpasteurized milk than the average consumer. This behavior is alarming

given the high prevalence of zoonotic diseases such as bTB in the area. On top of this,

our data showed that there was statistically significant relation between bTB status of

the herd and knowledge about pasteurization/pasteurized milk (LR Chi(2)=7.19 and

P=0.007); i.e., of those farmers who had bTB positive cattle, 54.4% had no knowledge

of pasteurization and of those farmers who know about pasteurization, 45.6% have

bTB positive animals (Table 54).

64

Table 54: Distribution of farmers who know the benefits of pasteurization by herd level bTB status

Herd level bTB stats Know the benefits of pasteurization

Total LR Chi2

No Yes

negative

Count 173 79 252

Percent 68.7 31.3 100.0 7.19***

positive

Count 118 99 217

Percent 54.4 45.6 100.0


Percent 62.0 38.0 100.0

Boiled milk consumption The results indicated that 95.2% (n=456) of the respondents drank boiled milk at least

once a week or more often; only 4.8% (n=23) indicated that they never drank boiled

milk at all (Table 55). This result is similar with Lemma et al (2017) and Duguma and

Janssens (2015). The frequency of boiled milk consumption was found to be

dependent on region (LR chi2(8) = 21.6208, P= 0.006) and that those in Hawassa

(86.54%) and in Addis Ababa (75.0%) consumed boiled milk more frequently than

those in Amhara (69.7), Tigray (68.33%), and Oromia towns surrounding Addis

Ababa (65.0%). This is related to the difference in the level of awareness about the

prevalence and health impacts of bovine TB and other zoonotic diseases.

Table 55: Distribution of households’ frequency of boiled milk consumption by location

Location

Frequency of raw milk consumption

Total

LR Chi 2

High Medium Low

Addis Ababa Administration

No. of farms 123 39 2 164

21.62**

Percent 75.0 23.8 1.2 100.0


No. of farms 89 34 14 137

Percent 65.0 24.8 10.2 100.0

Amhara (Gondar)

No. of farms 46 18 2 66

Percent 69.7 27.3 3.0 100.0

Tigray (Mekelle)

No. of farms 41 16 3 60

Percent 68.3 26.7 5.0 100.0

SNNPR (Hawassa)

No. of farms 45 5 2 52

Percent 86.5 9.6 3.8 100.0

Total

No. of farms 344 112 23 479

Percent 71.8 23.4 4.8 100.0

Meat consumption patterns and zoonotic risk

Per capita meat consumption The mean rate of per capita meat consumption among the dairy farmers in our sample

was found to be 1.24kg (SD =1.44) per month (Table 56), which was much higher

than the national average that is only 5.38kg per annum (FAO, 2016), corresponding

to less than 0.5kg per month. This difference is likely because our study sites are

urban areas where the per capita meat consumption is expected to be higher (Betru and

Kawashima, 2009). Although some studies show that the average consumption of

meat varied by region our data showed that there was no statistically significant

65

difference in mean meat consumption in the dairy households between our study

regions (F=1.10; P=0.3511).

Table 56: Average meat consumption in kilogram per month by site

Location N. Mean SD F-value

Addis Ababa 164 1.37 1.87

1.10

Oromia towns around Addis Ababa 135 1.12 1.06

Gondar 66 1.35 1.46

Mekelle 57 1.33 0.95

Hawassa 25 .88 0.49

Total 447 1.25 1.44

The mean per capita meat consumption per month for male-headed households was

found to be 1.35 kg, which is higher to a statistically significant degree (t=-2.43,

P=0.015) than that of female-headed households whose mean per capita consumption

lies at only 0.95 kg per month. This might be due to the relative deprivation of female-

headed households (Muleta and Deressa, 2014) due to low access to productive

resources. Although we expected to see a difference for meat consumed between

households of different religions and levels of literacy, we found no statistically

significant difference in per capita meat consumption between Christians and

Muslims, or between households with illiterate and literate heads.

Meat type preference For the whole sample farms, the ranking of meat type preference of the respondents

was found to be beef 48.4% (n=220) followed by mutton 31.6% (n=144), chicken

10.5% (n=48) and goat meat 9.5% (n``43). However, these figures vary significantly

by region (location). In fact, there is a statistically significant association between

preferred meat type and region (LR chi2 (12) = 135.49; P= 0.000). The respondent

dairy farmers in Addis Ababa (62.3%), Hawassa (44.23%) and the surrounding towns

of Oromia (63.85%) tended to significantly prefer beef and the majority in Gondar

(76.3%), in Amhara region, tended to prefer mutton. Similarly, in Mekelle (56.4%) in

Tigray region the most preferred meat type is mutton. This may be related to the

relatively advanced development of the beef abattoir industry in Addis Ababa,

Hawassa and other towns of Oromia surrounding Addis Ababa than in the Gondar and

Mekelle.

66

Table 57: Distribution of meat type preference by location

Region Count/Percent Households’ meat type preference Total LR ratio Chi2

Mutton Goat meat

Beef Chicken meat

Addis Ababa Administration

Count 37 8 99 15 159

135.49***

Percent 23.3 5.0 62.3 9.4 100.0


Count 20 8 83 19 130

Percent 15.4 6.2 63.8 14.6 100.0

Amhara (Gondar) Count 45 3 10 1 59

Percent 76.3 5.1 16.9 1.7 100.0

Tigray (Mekelle) Count 31 15 5 4 55

Percent 56.4 27.3 9.1 7.3 100.0

SNNPR (Hawassa) Count 11 9 23 9 52

Percent 21.2 17.3 44.2 17.3 100.0

Total Count 144 43 220 48 455 Percent 31.6 9.5 48.4 10.5 100.0

A statistically significant association (LR Chi

2 = 257.2079; p=000) was also observed

between source of meat and meat type preference (Table 58); i.e. those households

which prefer beef tended to go to Butchery (n=178) while those who preferred mutton

(n=107) go for home slaughter. It also shows that most of the farmers are beef (n=220)

followed by mutton (n=144) consumers.

Table 58: Distribution of households’ meat source preference by meat type preference

Source

Households’ meat type preference Total

LR Chi2

Mutton Goat meat

Beef Chicken meat

Home slaughter Count 107 32 8 19 166

257.2079***

Percent 74.31 74.42 3.64 39.58 36.48

Butchery Count 33 11 178 25 247

Percent 22.92 25.58 80.91 52.08 54.29

Communal slaughter Count 4 0 34 4 42

Percent 2.78 0 15.45 8.33 9.23

Total Count 144 43 220 48 455

Percent 100 100 100 100 100

With regard to the relationship between gender of the household head and meat type

preference, a likelihood ratio chi-square test showed that there is no statistically

significant contingency relation between the two, indicating that meat type preference

is independent of the gender of the household head. A one-way analysis of variance in

terms of mean age of the respondents and their preferred meat type showed that there

is a statistically significant difference in mean age with F value of 5.49 and P=0.001.

Those households which preferred chicken meat had a lower mean age of 40.58 years

(Std. dev. 10.87 years) and this was found to be significantly different from those who

preferred mutton (mean age= 49.82 years and SD= 15.39), and those who preferred

beef (mean age=46.23 year with SD.=14.53). Education, which was measured as a

dummy variable with the two categories being literate or illiterate, showed no

67

relationship to meat type preference (Fisher's exact test P value = 0.830) (Table 59).

Table 59: Distribution of households’ meat type preference by education status

Educational status

Kind of meat often consumed Total Fisher’s Exact

Mutton Goat meat Cattle meat Chicken meat

Illiterate

Count 10 2 17 3 32

0.830

Percent 31.3 6.3 53.1 9.4 100.0

Literate

Count 134 41 203 45 423

Percent 31.7 9.7 48.0 10.6 1000.

Total

Count 144 43 220 48 455

Percent 31.6 9.5 48.4 10.5 100.0

Meat consumption frequency As shown in Table 60, the majority of surveyed farmers (56.58%) consumed meat two

to five days a week. Only 1.04% indicated that they consumed meat every day and

only 0.63% (3 individuals) indicated that they did not consume meat at all.

Examination of the relationship between region and meat consumption frequency, as

well as gender of the household head and meat consumption frequency showed that

there is no statistically significant relationship in either case, with Fisher's exact test P

values of 0.171 and 0.257, respectively.

Table 60: Frequency of meat consumption

Frequency Meat consumption

Raw meat consumption

N Percent N Percent

Everyday 5 1.04 2 0.42

2-5 days a week 271 56.58 96 20.04

Once every fortnight 109 22.76 51 10.65

Once a month 66 13.78 66 13.78

Only for holidays 25 5.22 91 19.00

Never 3 0.63 173 36.12

Total 479 100 479 100

An investigation into the relationship between age of the household head and

frequency of meat consumption showed that there is no significant difference in mean

age between those households which frequently consumed meat and those who did so

less frequently (t = -0.2779 and P = 0.7812). On the other hand, education level and

frequency of meat consumption were found to be related (Fisher's exact value

P=0.001); Of the illiterate household heads, 55.88% indicated a high frequency of

meat consumption while about 82.61% of the literate households consumed meat more

frequently. This may be because in many cases, the income levels of literate

households are likely to be higher in comparison to the illiterate ones, and as meat is

relatively expensive food, it enabling them to consume meat more frequently than

their illiterate counterparts.

Sources of meat The respondent farmers were asked to rank the sources of their meat in terms of

preference. As shown in Table 61, the most important source of meat for the total

68

sample of farmers was found to be butchery (53.89%), followed by home slaughter

(36.63%) and then communal slaughter (9.47%).

Table 61: Rank order of meat source of sample dairy farmers

Meat source Rank 1 Rank 2 Rank 3

Freq. Percent Freq. Percent Freq. Percent

Home slaughter 174 36.63 191 42.16 91 27.83

Butchery 256 53.89 109 24.06 58 17.74

Communal slaughter 45 9.47 153 33.77 178 54.43

Total 475 100 453 100 327 100

The relationship between meat source ranking and variables such as gender, education,

religion, region, and age were examined. We found a statistically significant

association between region and the main source of meat (likelihood-ratio chi2 (8) =

126.4188 Pr = 0.000) (Table 62). The majority of farmers from Addis Ababa (75.61%)

indicated that butchery was their primary source of meat, compared to only 6.90% of

farmers from Gondar in the Amhara region. This is, as indicated previously, likely due

to the varying degree of urbanization and availability of abattoir service and butchery

facilities. We also found that there is a statistically significant difference (F=4.15; P=

0.0163) in mean age of the household head between those households using butchery

(44.88 years) as a primary source and those using home slaughter as a primary source

(48.92 years). This may have been due to better incomes and wealth of the older

people enabling them to opt for home slaughter, which is much more expensive and

prestigious; or it might be due to a higher value being placed on traditional methods of

slaughter by older people. The results indicated that none of the other socioeconomic

factors such as gender of the household head, education status (literate or illiterate), or

religion were related to the primary meat source of a household.

Table 62: Distribution of households’ main source of meat by location

Location Household’s source of meat Total

LR Chi2

Home slaughter

Butchery Communal slaughter

Addis Ababa Administration Count 35 124 5 164

126.41***

Percent 21.3 75.6 3.0 100.0

Oromia towns surrounding Addis Ababa Count 37 81 18 136

Percent 27.2 59.6 13.2 100.0

Amhara (Gondar) Count 51 4 9 64

Percent 79.7 6.3 14.1 100.0

Tigray (Mekelle) Count 37 14 8 59

Percent 62.7 23.7 13.6 100.0

SNNPR (Hawassa) Count 14 33 5 52

Percent 26.9 63.5 9.6 100.0

Total

Count 174 256 45 475

Percent 36.6 53.9 9.5 100.0

Raw meat consumption habits Raw meat consumption is an increasingly common habit in Ethiopia, especially in the

urban areas. People consume red and fatty meat - mostly beef, goat, or mutton meat -

69

in its raw state. However, as discussed above, this has been shown to be a risky

behavior in terms of zoonotic disease transmission in high prevalence contexts such as

Ethiopia. Sampled dairy farmers from cities and towns where bTB prevalence among

dairy cattle is relatively high were asked about their behaviors in terms of raw meat

consumption. The data collected indicated that the majority (63.88%) of these farmers,

habitually consumed raw meat and that as many as 20.46% of them indicated that they

were in the habit of consuming raw meat either every day or 2-5 times a week. Over a

third of the respondents (36.12%), however, indicated that they have never consumed

raw meat (Table 63).

An investigation was conducted into the relationship between raw meat consumption

frequency and demographic factors such as Sex, Age, Religion, Education and Region.

Region and Religion were found to have statistically significant association with raw

meat consumption frequency: Muslims tended to avoid raw meat, with 75% (15 out of

20) of Muslims surveyed, indicating that they had never consumed raw meat.

Region was found to have a significant contingency relationship with frequency of

raw meat consumption (LR chi2(20) = 120.6243; P = 0.000). Among the regions

surveyed, the proportion of dairy farmers who consumed raw meat more frequently (at

least once in a fortnight) was 66.46% in Addis Ababa, 76.64% in Oromia, 65.52% in

Hawassa, and 67.31% in Gondar. However, the farmers in Mekelle diverted from this

habit with only 25% indicating that they had a habit of raw meat consumption; this

might be due to the relatively underdeveloped fattened cattle production and

marketing in Mekelle as compared to the central part of Ethiopia where there are

numerous feedlots specialized in beef cattle production for local and international

markets.

No relationships between gender, education, and age and raw meat consumption

frequency were found in this survey. That means that no difference was observed in

terms of raw meat consumption behavior between the literate and illiterate household

heads. In this regard, Ameni et al (2003) also found out that the level of education did

not impact the habit of raw meat consumption in central Ethiopia.

Sample respondent farmers were also asked if they think that eating raw meat can

cause diseases. The results indicated that the majority of farmers (92.9%) believed that

the consumption of raw meat can cause disease and about 40.08% of them had

actually experienced diseases, which they attributed to eating raw meat (Table 63).

Many respondents reported that they had experienced diseases such as abdominal

discomfort, tapeworm, amoeba, gout, and even TB as a consequence of eating raw

meat. Farmers were also asked if they knew that TB could be transferred from animals

to humans through the consumption of raw meat. Out of the total sample (n=477),

62.26% indicated that they thought eating raw meat could cause the transfer of TB

from animals to humans; 23.06% (n=110) indicated that they did not know whether

this was the case, and only 6.29% (n=30) stated that TB does not transfer from animal

to human due to the eating of raw meat.

70

Table 63: Farmers views about disease transfer risks of eating raw meat

We also investigated whether a relationship exists between having attended training on

zoonotic diseases and bTB transmission pathways, and farmers‟ meat consumption

behavior. The results of this test indicated that there is a statistically significant

relationship (Fisher's exact = 0.001) between these two variables. Of those farmers

who had not undertaken training on zoonosis, 24.5 % indicated that they consumed

raw meat more frequently (at least once in a fortnight); in contrast, only 8.87% of the

farmers who undertook training on zoonosis provided by local government extension

service indicated that they consumed raw meet frequently. This indicated that the

effect of trainings on zoonotic disease control is highly significant and should not be

underestimated.

Our results (Table 64) also showed that there was a statistically significant relationship

between raw meat consumption habit and occurrence of TB in the family in the past

(LR chi2 (2)= 5.681; P = 0.017). Out of the farm households who reported that there

has been confirmed human TB case in the last three years in their farm, 20.8% have

indicated that they have the habit of raw meat consumption while the corresponding

figure for those who reported no TB cases in the past was 79.2%.

Table 64: Cross tabulation of raw meat consumption by confirmed Tb cases in the farm

Do you have the habit of raw meat consumption in the

family?

Is there any antecedent/history of confirmed TB cases in the farm during

the last five years’ time?

Total LR Chi2

No Yes

No 264 38 302

5.6814**

62.4 79.2 64.1

Yes 159 10 169

37.6 20.8 35.9

Total 423 48 471

100.0 100.0 100.0

The fact that a considerable proportion of the population frequently eat raw meat and

drink raw milk indicating that both milk and meat consumption behaviors of the

farmers are risky in terms of zoonotic transfer of diseases such as bTB.

Educational level differences; i.e. being literate or illiterate, was found to be least

related to risky behaviors such as raw milk and meat consumption. Hence, irrespective

of educational levels it is important to induce behavioral change in meat and milk

consumption patterns among the urban and peri-urban dairy farming population.

Do you think eating raw meat causes diseases?

Have you ever experienced diseases due to eating raw meat?

Response Freq. Percent Freq. Percent

No 34 7.1 287 59.92

Yes 445 92.9 192 40.08

Total 479 100 479 100

71

There are evidences that training on zoonosis disease transmission mechanisms would

enable behavioral change in terms of milk and meat consumption that are generally

regarded as risky. Hence, it is important that awareness creation about the risk of bTB

and behavioral change in terms of milk and meat consumption patterns could be

induced through tailor made trainings for farmers, farm workers and the public.

The regional disparities in terms of risky meat and milk consumption behavior is not

wide; yet, the relative concentration of development of abattoir and pasteurization

facilities in and around Addis Ababa and the relative deprivation of other regions in

such facilities has implications on meat and milk consumption patterns and thereby

public health as well.

Household health care seeking behavior The general health status of the surveyed households was perceived to be poor by 4%

of the respondents and as excellent, very good, and good by the remaining 96% (Table

65). The health status of the households was reported better by males compared to

when reported by females and the health status was better for households living in

urban areas than for households in peri-urban areas (Table 66). Health status of the

households in the study farms was not significantly different across gender, residence

and the study sites (Table 67). The result is similar with a previous study conducted in

Ethiopia by Ministry of Health (MOH, 2014).

Table 65: knowledge health status of the household in the study dairy

Household health status Frequency Percent

Excellent 1138 46

Very good 869 35

Good 371 15

Poor 103 4

Total 2481 100

Table 66: Perception and knowledge of the household on their health status

Factors/particulars Health status

Excellent Very good Good Poor Total

Gender Male 874 (47%) 627 (33.7%) 274 (14.7%) 85 (4.6%) 1860

Female 260 (42.1%) 241(39.1%) 97 (15.7%) 19 (3.1%) 617

Total 1134 (45.8%) 868 (35%) 371(15%) 104 (4.2%) 2477

Residence Urban 829 (46.5%) 600 (33.7%) 286 (16.1%) 67 (3.8%) 1782

Peri-urban 309 (44.1%) 269 (38.4%) 85 (12.1%) 37 (5.3%) 700

Total 1138 (45.9) 869 (35%) 371 (14.9%) 104 (4.2%) 2482

Study site Addis Ababa 415 (54.7%) 265 (35%) 65(8.6%) 13 (1.7%) 758

Sebeta 92 (65.2%) 38 (27%) 9 (6.4%) 2 (1.4%) 141

Holetta 58 (30.9%) 87 (46.3%) 31 (16.5%) 12 (6.4%) 188

Sululta 48 (42.1%) 44 (38.6%) 13 (11.4%) 9 (7.9%) 114

Sendafa 62 (44.6%) 52 (37.4%) 13 (9.4%) 12 (8.6%) 139

Debrezeit 49 (41.5%) 48 (40.7%) 19 (16.1%) 2 (1.7%) 118

Gonder 166 (40.7%) 161 (39.7%) 54 (13.2%) 27 (6.6%) 408

Mekelle 153 (52.6%) 28 (9.6%) 99 (34%) 11 (3.8%) 291

Hawassa 42 (26.1%) 80 (49.7%) 29 (18%) 10 (6.2%) 161

Total 1085 (46.8%) 803 (34.6%) 332 (14.3%) 98 (4.2%) 2318

72

During the survey, 8.1% of the households in peri-urban and urban areas reported to

be ill two weeks before interview. Prevalence of self-reported illness was higher for

males (6%) than females (2.1%) and for individuals living in urban areas (5.6%) than

in peri-urban (2.5%). The general prevalence of health care seeking behavior was 84%

(95% CI, 81–87%) with 87% urban and 75% peri-urban households. Based on the

result, about 13% and 25% of the households from urban and peri urban areas have

never been consulting health professionals two weeks before interview. Majority of

the households (67.5%) of urban but only 24.5% of peri-urban households have got

health care from public health center. Differences in awareness level between urban

and per urban households about the impact of the disease, availability of the service

and distance of the health posts and cost of the service could be the major factors

limiting their health seeking behavior. Most of the health posts are concentrated in

urban areas and provide better access to the urban residence to seek health service.

Table 67: Health status of dairy farm workers two weeks before interview

Dairy farm workers health status two weeks before interview

Frequency Percent (%)

Illness

No 440 91.9

Yes 39 8.1

Total 479 100

Gender

Male 29 74.4

Female 10 25.6

Total 39 100

Residence

Urban 27 69.3

Peri-urban 12 30.7

Total 39

Based on the response rate of the respondents, private health facilities and government

clinic/hospital were important health posts preferred by more than 80% of the

respondents in each case (Table 68). However, it is evident from Table 68 that few

farm owners (about 14%) go to other places such as traditional healers and traditional

medicine and churches. About 35% of the respondents practice treating themselves

before seeking modern health care service. Limited availability and distance of

modern health service centers from their locality, high cost of treatment, influence of

culture and religious believers and perceived knowledge of household on traditional

medicine could be some of the factors which influence households to practice

traditional healers and medicinal plants for the treatment of themselves and their

family members in developing countries (Nahid, 2015).

73

Table 68. Household seeking behavior and preferred health care posts

Preferred Health care posts No of respondents

Yes No Total

Private clinic 400 (84%) 76 (16%) 476

Government clinic/hospital 386 (81.1%) 90 (18.9%) 476

Clinic run by a nongovernmental organization or church

55 (11.6%) 421 (88.4%) 476

Traditional healers 66 (13.9%) 410 (86.1%) 476

Others (holy water, steam bath) 5 (1%) 471 (99%) 476

The health seeking behavior of the respondents was assessed in terms of the number of

times they visited health centers. Accordingly, it was found that 63.8% indicated the

service was used once or twice a year, 28.3% once or 2 times in the past 5 years, and

8% never used the service (Table 69).

Table 69. Frequency of household health care service seeking behavior

Frequency of health care seeking behavior Frequency Percent

Twice a year 162 34

Once per year 142 29.8

Less than once but at least twice in past 5 years 91 19.1

Once in past 5 years 44 9.2

Never in past 5 years 38 8

Total 477 100

The majority of the respondents were aware of the risk of transmission and source of

zoonotic diseases, particularly tuberculosis. However, dairy farm workers had limited

knowledge on bovine tuberculosis and its transmission to humans. Based on the dairy

farmers‟ response, the prevalence of confirmed tuberculosis cases among dairy farm

workers was higher in large farms than in medium and small size dairy farms

managed; this could be associated with the management systems tuberculosis is a

disease that benefits from intensification. The majority of the respondents perceived

their health status as excellent. The prevalence of self-reported diseases was higher in

peoples living in urban residence than in peri-urban. The household health seeking

behavior of the respondents was higher in urban followed by peri-urban residents.

Such behavior could be affected by different factors like availability of health service,

distance from their residence, and cost of the service.

74

Conclusions This study provides important information about the urban and peri-urban dairy

systems explained in terms of farm and herd structures, demographics/habits of the

farm owners, their households and their farm workers, as well as the socio-economic

environment. Most (77%) of the dairy farms surveyed were male and private owned

and cooperative and government ownership is very limited. Farm workers are also

male dominated (61%). Large dairy farm owners are relatively educated which could

indicate that large and intensive dairy farming in urban and peri-urban dairy farming

system, which is characterized as capital-intensive venture, is associated to

knowledge-based decisions. Considerable number of the hired farm workers in large

farms is illiterate and stay longer in large government farms. In addition, as the

prevalence of bTB is in general higher in the larger farms, these farms workers are

vulnerable to higher risk of exposure to bTB. The vast majority of the cattle in the

investigated herds were crosses between exotic (mainly Holstein Frisian and some

Jersey) breed dairy cattle and the local Zebu breeds, with high-grade blood level

crosses and medium-to-low grade blood level crosses. The herd structure by generic

category of cattle showed that cows make up the greater share (30%) followed by

calves (26%) and heifers (23%). These categories make up the greater share (75%) of

the total herd. The survey results showed that dairy farms operated an average of 1.33

hectares of land with standard deviation of 3.48. All farms employ hired labor and the

average number of cattle per worker is 6.5 for the whole sample while it is 5.5 for

small holders, 8.6 for medium and 9.7 for large farms. This system is predominantly

dependent on purchased feed that 81.25% practice zero grazing. Feed purchase makes

up the greater share of variable cost.

This urban and peri-urban intensive system is rapidly expanding in many areas of

Ethiopia. However, the small farms are increasing in number over time while a clear

decline is observed in the number of medium and large farm establishment in recent

decade. Thus, the general increase in the number of dairy farms can be attributed to

small farms. The reason could be that large farms have faced difficulties of expanding

in the cities compared to the other farm types. Possible entry and business barriers to

establish larger farms could be limited access to land, high value of land, and animal

feed shortage, factors that have resulted from increased urbanization and an economy

boom in and around cities. Conversely, such scenarios coupled with the development

of cooperative dairy farms seem to have helped smaller farms to flourish better than

larger ones.

Diseases such as mastitis, FMD, lumpy skin disease make up the most important

diseases. These are diseases associated with intensification. With intensification and in

the absence of control strategy such as well-developed surveillance, disease diagnosis,

and animal movement control, the prevalence of diseases such as bTB is on the rise.

BTB, though over 50% of the farms are infected in some areas, is ranked least by

farmers as it has a chronic nature and its effects on farm productivity, animal mortality

and morbidity is not conspicuous. Yet, it poses a great zoonotic risk that may thwart

75

the development of the dairy sector in general and the intensive system in particular

which supports the livelihoods of millions of people including poor families.

This study highlighted that farmers have limited access to support services such as

credit facilities, extension service on animal husbandry, as well as veterinary health

support. In the status of the dairy sector, and in particular if the sector would expand,

these limitations will likely lead to increased disease burden in cattle and an increased

zoonotic risk to the farmers. Therefore, any bTB and other diseases control strategy

need to consider tackling these inadequacies as part of a larger strategy. In addition to

these limitations, surveyed farmers often raised problems of low milk prices

(especially during fasting seasons), high feed costs, problems of waste disposal and

lack of legality and access to land for expansion of dairy farms. Therefore,

government and other support services for the urban and peri-urban dairy sector need

to tackle these constraints if the current levels of milk production and consumption are

to be raised in the country.

The study of this intensive dairy sector that is emerging in many parts of the country

revealed that the average herd prevalence rate of bTB is 46.4% in the explored sites,

ranging from 11.1% at Hawassa to as high as 63.7% in Addis Ababa city. The risk

factors associated with bTB were found to be location of the farm, bio-security, farm

herd size, farm ownership, and access to extension education on zoonosis. Since,

farms in Addis Ababa and those farms with link to farms in Addis Ababa were found

to be bTB positive, bTB control strategy need to emphasize the role of animal

movement control in containing bTB spread from the center to the periphery. Since

farms which lack bio-security caution; i.e., farms, which have contact with

neighboring farms and which are accessible to wild animals such as Mongoose and

Mole rat, have higher probability of bTB infection, bTB control strategies need to take

bio-security as an important intervention area and educational and training programs

to dairy farmers need to emphasize the need for bio-security caution in managing dairy

farms. In addition to these, since farm herd size has strong association with probability

of bTB infection, bTB control strategies need to focus on large farms. These farms are

also in a better position to adopt some of the well-known control options such as test

and segregate than the smallholder farms. From our result, the fact that farm

ownership status, whether a private firm or a public enterprise owns it, has significant

impact on the probability of the farm being bTB positive. Thus special care need to be

given to the publicly owned dairy farms in designing control options as they could be

potential sources of bTB infection to the rest of the farms. The evidence that farmers

that had received training on zoonosis diseases has a negative impact on bTB

infection, suggests that bTB control strategies need to have a strong extension

education component on zoonotic diseases and their prevention strategies.

There are evidences that farmers, as part of their own efforts to control bTB in their

herds, often sell their infected animals without disclosing the health status of the

animal. To deal with such behavior and to materialize establishment of e.g. disease-

free zones, establishment of a bTB disease surveillance and animal movement

regulation would be important elements and ideally without exorbitant cost to the

farmers.

76

The fact that the majority of the farming population frequently eat raw meat (64%)

and considerable proportion of them drink raw milk (20%) indicate that both their

milk and meat consumption behaviors are risky in terms of zoonotic transfer of

diseases such as bTB. Educational level differences; i.e. being literate or illiterate, was

not found to be related to such risky behaviors. Hence, irrespective of educational

levels it is important to induce behavioral change in meat and milk consumption

patterns among the urban and peri-urban dairy farming population. There are

evidences that training on zoonosis disease transmission mechanisms would enable

behavioral change in terms of milk and meat consumption that are generally regarded

as risky. Hence, it is important that awareness creation about the risk of bTB and

behavioral change in terms of milk and meat consumption patterns could be induced

through tailor made trainings for farmers, farm workers and the public. The regional

disparities in terms of risky meat and milk consumption behavior is not wide; yet, the

relative concentration and development of abattoir and pasteurization facilities in and

around Addis Ababa and the relative deprivation of the regions in such facilities has

implications on the relative level of riskiness of meat and milk consumption

behaviors.

Some 37.6 % of dairy farm workers mentioned that TB is a major disease transmitted

from animals to humans through close contact with infected animals and by

consumption of raw meat and milk. This rate of awareness is low given the high

prevalence of the disease in the surveyed area and given that bTB is a very common

disease in the intensive dairy sector. Hence, a vigorous awareness program on the risks

related to bTB transmission needs to be in place in order to prevent the spread of the

disease to other areas and currently uninfected farms.

77

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