characteristics of urban and peri-urban dairy production
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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.
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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;
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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 ***
Religious education 4.9 3.4 0.0 4.2 (18)
Primary education 33.1 23.0 0.0 28.5 (122)
Secondary education 31.8 29.9 24.2 30.8 (132)
Higher education 20.5 37.9 63.6 27.3 (117)
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)
Religious education 3.4 7.4 3.8 4.6 (109)
Primary education 42.6 42.5 37.3 41.5 (994)
Secondary education 26.2 23.1 21.6 24.4 (585)
Higher education 10.7 11.1 15.7 11.8 (283)
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
Medium-herd (20-49) 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)
Medium-herd (20-49) farm
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
Total Count 250 216 466
Total Count 204 122 326
% 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
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
Total Count 291 178 469
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
Oromia towns surrounding Addis Ababa
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
Oromia towns surrounding Addis Ababa
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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