DEVELOPMENT OF RESPONSIVE DAIRY CATTLE BREEDING GOAL IN THE
FACE OF CLIMATE CHANGE IN KENYA
CHEPSIROR KIRWA CURTIS
KM111/13655/19
A Research Proposal Submitted to the Graduate School in Partial Fulfillment for the Requirements
for the Master of Science Degree in Animal Breeding and Genomics of Egerton University
EGERTON UNIVERSITY
JULY, 2021
i
DECRALATION AND RECOMMENDATION
DECLARATION
This research proposal is my original work, and has been presented in this University or any other for
an award of a Degree.
Signed: __________________________
Mr. Chepsiror .K. Curtis (KM111/13655/19)
Date: ____________________________
RECOMMENDATION
This proposal is the candidate’s original work and has been prepared with our guidance and assistance;
it has been submitted with our approval as the official University supervisors.
Signed_______________________________
Dr. Tobias. O. Okeno (Dr. rer. agr.)
Date: ________________________________
Signed_______________________________
Dr. Evans Ilastia (Dr. Sci. agr.)
Date________________________________
ii
ABSTRACT
Smallholder dairy farmers own 80% of the dairy cattle population and produce 70% of the total
marketed milk in Kenya. Therefore, smallholder dairy production is a good avenue to ensure food
security, increased income and poverty reduction among the resource poor households.
Smallholder dairy farmers, however, source their breeding and replacement stock from large scale
farms with well-defined breeding goal and more robust management systems for optimal
productivity. Smallholder farms, on the other hand are faced with multiple production constraints
such as lack of enough feed resources, poor housing, climate change and outbreak of diseases
leading to low productivity of the dairy animals. These differences in production environments
experienced between large- and smallholder dairy could be attributed to mismatch of breeding
goals hence the unfavorable genotype-by-environment interaction. Furthermore, the advent of
climate change is expected to expose smallholder farmers to more risks and thus there is need
develop a robust breeding goal to address effects caused by climate change. The current study
therefore aims to contribute to improvement of smallholder dairy production through estimation
of economic values for feed efficiency, resilience, adaptability and disease resistance traits,
estimate response to selection realized when feed efficiency, resilience, adaptability and disease
resistance traits are accounted for in the breeding goal and estimated correlated response to
selection in smallholder farms when accounting genotype-environment interaction between large
and smallholder farms in Kenya. The economic values for feed efficiency, resilience and
adaptability traits will be derived using bio-economic model developed in python computer
program, while that for disease resistant traits will be computed using selection index program.
Using a deterministic simulation approach, a two-tier closed nucleus breeding scheme will be
modelled in ZPLAN+ to evaluate direct response to selection realized in the developed breeding
and correlated genetic and economic gains achievable in smallholder farms when genotypeenvironment between large-and smallholder farms is accounted for. The findings from this study
will provide important information needed for informed selection decisions that reflects priorities,
need and expectations of smallholder dairy farmers in Kenya.
iii
TABLE OF CONTENTS
DECLARATION AND RECOMMENDATION………………………………………………ii
ABSTRACT……………………………………………………………………………………..iii
LIST OF ABBRIVIATIONS…………………………………………………………………..vii
CHAPTER ONE……………………………………………………………………………...…1
INTRODUCTION……………………………………………………………………………....1
1.1 Background information…………………………………………………………………...1
1.2 Statement of the problem…………………………………………………………………..5
1.3 Objectives……………………………………………………………………………………5
1.4 Research questions………………………………………………………………………….6
1.5 Justification….………………………………………………………………………………6
1.6 Expected outputs…………………………………………………………………………....7
CHAPTER TWO………………………………………………………………………………...8
LITERATURE REVIEW……………………………………………………………………….8
2.1 Background of dairy production in Kenya….……………………………………………..8
2.2Dairy production systems in Kenya……...………………………………………………….8
2.2.1Commercial dairy farming.……………...……………………………....……………….......8
2.2.2 Smallholder dairy farming…………...……………………………………………………...9
2.3 Zero, semi-zero and Free grazing system…..………………..……………………..............9
2.4 Stall feeding………..……………………………………………………..............................10
2.5 Feeds and feeding……………..…………………………………………………………….11
2.6 Milk production and cost ……..…………………………………………...……………....11
2.7 Milk marketing & Regulation………….………………………………………………….12
2.8 Smallholder dairy breeding program in Kenya……..……………………………………14
2.8.1 Background……….………………………………………………………………………..14
2.8.2 Breeding goal……………………………………………………………….……………...15
Table 1: Economic Values in Kenya shillings (1USD$=100.00) in the breeding objective under
two payment systems for milk…………………………………………………………………...17
iv
2.9 Computer Simulations in animal breeding………...…………………………...………...18
2.10 Bio-economic modelling…...………………………………………………….…………...18
2.11 Conventional & Genomic breeding programs………..…………………….…………...19
2.12 Reproductive technologies in dairy cattle breeding (AI, MOET)…...………………....19
2.12.1 Artificial Insemination…………………………………………………………………..19
2.12.2 Multiple Ovulation Embryo Transfer……………………………………………………20
CHAPTER THREE……………………………………………...……………………………..21
MATERIALS & METHODS…………………………………...……………………………..21
3.1 Procedure………………….………………………………………………………………...25
3.2 Breeding goal ……………….….…………………………………………………………...25
3.3 Derivation economic values for feed efficiency, resilience, adaptability and disease
resistance………………………………………………………………………………………...22
3.4 Estimation of direct response to selection for breeding goal accounting for feed
efficiency, resilience, adaptability and disease resistance indicator traits in large scale dairy
farms in Kenya...………….…………………………...……………………………………......24
3.5 Estimation of correlated response to selection for breeding goal in smallholder dairy
farms when selection is done in large scale dairy farms based on breeding goal accounting
for feed efficiency, resilience, adaptability and disease resistance while considering
genotype-environment interaction between the two populations………..………………….27
3.6 Data analysis ………………………….….………………………………………………...28
4.0 WORKPLAN………….……………………………………………………………………29
5.0 BUDGET…………….……………………………………………………………………...30
6.0 REFERENCES……………….…………………………………………………………….31
v
LIST OF ABBREVIATIONS
ACET
AI
CNS
CS
DGEA
EBV
FAO
GDP
GS
IFAD
ILRI
KCC
KDB
KMDP
KNBS
MoA
MOET
ONS
REV
UNDP
African Center for Economic Transformation
Artificial Insemination
Closed Nucleus System
Conventional Selection
Dairy Genetics East Africa
Estimated Breeding Value
Food Agricultural Organization
Gross Domestic Product
Genomic Selection
International Fund for Agricultural Development
International Livestock Research Institute
Kenya Co-operative Creameries
Kenya Dairy Board
Kenya Market-led Dairy Programme
Kenya National Bureau of Statistics
Ministry of Agriculture
Multiple Ovulation Embryo Transfer
Open Nucleus System
Risk-rated Economic Value
United Nations Division of Population
vi
CHAPTER ONE
INTRODUCTION
1.1 Background Information
Kenyan dairy cattle population is estimated at 6.7 million (Wahinya et al., 2015) with an estimated
annual milk production of 3.56 billion liters (KDB, 2017). The dairy cattle are raised under both
smallholder and large scale commercial farms (Odero-Waitituh et al., 2017). Smallholder dairy
farmers own over 80% of the dairy cattle, producing over 70% of the total milk (KNBS, 2017).
They are kept under semi- and intensive production systems (Wambugu et al., 2013). The
remaining 20% is from large scale dairy farms and indigenous cattle (Odero-Waitituh et al., 2017).
This implies that smallholder dairy cattle production plays significant role in most households and
can be used as a tool for economic development, food security and poverty reduction among the
resource poor households in Kenya. The smallholder dairy farmers raise different breeds of dairy
cattle such Friesian, Ayrshire, Jersey, Guernsey and their crosses.
The smallholder dairy farmers in highlands have shown preference to large dairy cattle breeds
(Friesian and Ayrshire) (Lukuyu et al., 2019) which is contrary to the recommendations made by
Bebe et al. (2002). Keeping larger dairy cattle breeds; Friesian and Ayrshire, by smallholder dairy
farmers has been discouraged for two reasons. Firstly, they have high nutritional demand and poor
adaptability to low production efficiency under smallholder conditions (Bebe et al., 2003).
Secondly, low genetic correlations have been reported between production environments on milk
and fertility traits because different ecological zones require unique set of genes for optimum
performance (Wahinya, 2020). Smallholder farmers, however, preferred the two breeds due to
their high milk productivity, adaptability and resilience to the production environment (Lukuyu et
al., 2019). Ayrshire was ranked highly for low feed requirements and resistance to diseases while
Frisian was ranked high for milk production (Bebe et al., 2003; Lukuyu et al., 2019). This implies
that the smallholder farmers are interested in a cow that can produce more milk but also adapted
to smallholder production environment. Smallholder farmers, however, do not breed their own
replacement stock. This is because they are constrained production resources such as land,
inadequate quantity and quality feeds (Wahinya et al., 2015), climate change, lack investment
capital and outbreak of diseases (Kibiego, 2015). They therefore source replacement stock from
large scale commercial dairy farms.
Large scale commercial dairy farms in Kenya are mainly capital intensive, mechanized and
therefore the cattle are under intensive management in terms of breeding, nutrition, housing and
disease control for profit maximization (Kariuki et al., 2017). Their main breeding goal focuses
mainly on increased milk productivity since the production environment is controlled. They
therefore mainly raise Friesian and Ayrshire dairy cattle breeds to optimize milk production. This
implies the large scale commercial farms dictate the breeding goal and breeds raised by
1
smallholder farmers. The preference of large breeds by the smallholder farmers therefore could be
conditional due to availability. This implies that, the breeding goal for large scale commercial
farms may not be fully in tandem with smallholder farms’ requirements which in addition to high
milk production also need adaptable and resilient cows as reported by Lukuyu et al. (2019). The
findings by Muasya et al. (2014) on the negative genetic correlation between breeding and
production environments of dairy cattle in Kenya could explain this mismatch. This situation is
expected to worsen with the impact of climate change. Climate change has been projected to pose
greatest risks in developing countries among the smallholder farmers (Rege et al., 2011). There
is therefore need to develop a robust breeding goal that would serve both large and smallholder
dairy farms even in the face of climate change. This will enhance production of dairy cattle breeds
adapted to low forage feed requirement, heat and disease tolerance and perform well in terms of
milk production with little or no supplementation. Thus, overcoming the current negative effects
of genotype-environment interaction and climate change experienced as a result of the current
breeding goal.
The current breeding goal of large scale commercial dairy farmers in Kenya was developed by
Kahi & Nitter (2004) and reviewed by Wahinya et al. (2015) and(Sagwa et al., 2020). The traits
in this breeding goal accounts for production, fertility and milk quality traits but assume resilience,
feed efficiency, adaptability and disease resistant traits. The traits in the current dairy cattle
breeding goal include; milk yield (MY), fat yield (FY), age at first calving (AFC), calving interval
(CI), pre-weaning daily gain (DG), post-weaning daily gain (PDG), pre-weaning survival rate
(SR), post-weaning survival rate (PSR), longevity (PLT), protein yield (PY) and mastitis resistance
(MR) (Kahi and Nitter 2004, Wahinya et al., 2015; Sagwa et al., 2020). It is evident from this
breeding goal that resilience, feed efficiency, adaptability, and disease resistance traits which are
considered important in smallholder dairy production (Lukuyu et al., 2019) are ignored.
Feed efficiency measures the relative ability of cows to turn feed nutrients into milk or milk
components(Al-qaisi, 2011). It is the quantity of milk per unit of dry matter of feed consumed.
Feed efficiency is important an important trait both for smallholder and large scale commercial
dairy farms since feed account for over 70% of the total production costs in dairy production (Alqaisi, 2011). In smallholder dairy farming which is faced with feed quantity and quality challenges
in the tropics, raising dairy cattle which are feed efficient would be of great importance as it may
contribute to resilience. Resilience is the capacity of the animal to be minimally affected by
disturbances or to rapidly return to the state pertained before exposure to a disturbance (Colditz &
Hine, 2016). Disturbances can be of different nature, being either physical (e.g., disease,
temperature stress) or psychological (e.g., novel environment, social stressor, human interaction)
(Colditz & Hine, 2016). Disease resistance is the ability of the host animal to exert control over a
disease (Bishop, 2019). Breeding for disease resistance, especially zoonotic disease such as
Brucellosis which causes abortion at the end of gestation period, reduction in milk yield and
infertility ((Musallam et al., 2019; Palsson-mcdermott, 2013; Montiel et al., 2017) would be a
promising approach towards achieving good health for dairy cows and human consumers. This is
2
important especially in the face of climate change which is expected to result to long drought
seasons leading to heat stress in cows (Haile & Tang, 2020). Heat stress is defined as the sum of
external forces acting on an animal that causes an increase in body temperature and evokes a
physiological response (Dikmen & Hansen, 2009). Excessive flow of energy (in the form of
unabated heat) into the body, in addition to energy depletion required for lactation and growth
(Ferrell, 1985) can lead to deteriorated living conditions, reduced quality of life, and, in extreme
cases, death (Mader & Davis, 2003). Breeding for these traits require their inclusion in the breeding
goal and therefore estimation of their economic values which are currently missing under the
Kenya production condition.
Economic value of a traits is a unit change in profitability attributed to a unit change in genetic
merit, holding other traits constant (Bekman & Arendonk, 1993). They are computed based on the
analysis of the existing data or using bio-economic modeling (Garc, 2012). The latter is always
preferred since breeding is for future. Bio-economic modeling also account for interaction between
traits and production environments which are always dynamic and complex (Pomeroy et al., 2008).
Since dairy cattle have long generation intervals and require heavy investment in the breeding
program, there is need to model and simulate the expected response to selection on the new
breeding goal before implementation.
The breeding program for dairy cattle in Kenya depicts that of a two tier closed nucleus breeding
scheme. This is because there is unidirectional flow of genes from the large scale commercial to
smallholder farms (Wahinya et al., 2015; Muasya et al., 2014; Sagwa et al., 2019). This breeding
scheme mainly utilizes conventional selection, but is also in the process of introducing genomic
selection (GS). Sagwa et al. (2019) evaluated and compared genetic and economic merits of
conventional and genomic selection for dairy cattle under the Kenyan production environment.
They recommended adoption GS breeding program that utilizes Multiple Ovulation and Embryo
Transfer to increase reproductive rate of both males and females in the breeding population.
Genomic selection (GS) has been recommended for three reasons. Firstly, it allows improvement
of genetic variance within a population (Jembere et al., 2017). This is because GS has the ability
to generate information on Mendelian sampling term (Wolc et al., 2015). This allows better
differentiation within families and therefore less co-selection of sibs to be used as parents for the
next generation, hence reduced rate of inbreeding in long-term selection (Daetwyler et al., 2007).
Secondly, it increases selection accuracy and lastly reduces generation interval resulting to
increased response to selection (Miller, 2010). Therefore modelling dairy cattle breeding program
that utilizes GS and MOET to evaluate breeding goal that accounts for resilience, feed efficiency
and disease in dairy cattle in Kenya would be necessary.
3
1.2 Statement of the Problem
Although smallholder dairy farmers in Kenya own 80% of the dairy cattle population and produce
70% of the marketed milk, they source their replacement stock from the large commercial dairy
farms. The current breeding goal for the large scale dairy farms does not account for resilience,
feed efficiency, adaptability and disease resistant traits that smallholder farmers consider to be
important. This has resulted to negative effects of genotype-environment interaction which is
expected to worsen with negative impacts of climate change. Breeding for feed efficiency,
resilience, adaptability and disease resistance require their inclusion in the breeding goal. This is
only feasible with availability of their economic values which have not been estimated under
Kenyan production conditions. The expected response to selection for the breeding goal
accounting for feed efficiency, resilience, adaptability and disease resistance is not known and its
impact on the existing genotype-environment interaction between large scale commercial and
smallholder dairy farms needs to be investigated.
1.3 Objectives
1.3.1 Overall Objective
The overall objective of this study is to contribute to improvement dairy cattle productivity through
inclusion of feed efficiency, resilience, adaptability and disease resistant traits in the current
breeding goal, derivation of their economic values and evaluation of response to selection while
accounting for genotype-environment iteration between large scale commercial and smallholder
dairy production in Kenya.
1.3.2 Specific Objectives
(i) To derive economic values for feed efficiency, resilience, adaptability and disease resistant
indictor traits for their inclusion in the current dairy cattle breeding goal in Kenya
(ii) To evaluate direct response to selection for breeding goal accounting for feed efficiency,
resilience, adaptability and disease resistance indicator traits in large scale dairy farms in
Kenya
(iii)To evaluate correlated response to selection in smallholder dairy farms when selection is
done in large scale dairy farms based on breeding goal accounting for feed efficiency,
resilience, adaptability and disease resistance while considering genotype-environment
interaction between the two populations
1.4 Research questions
(i)
What are the economic values for feed efficiency, resilience, adaptability and
disease resistance indicator traits?
(ii)
What is the direct response to selection for breeding goal accounting for feed
efficiency, resilience, adaptability and disease resistance indicator traits in large
scale dairy farms in Kenya?
4
(iii)
What is the correlated response to selection in smallholder dairy farms when
selection is done in large scale dairy farms based on breeding goal accounting for
feed efficiency, resilience, adaptability and disease resistance while considering
genotype-environment interaction between the two populations?
1.5 Justification
Dairy production plays significant role in ensuring food security and poverty reduction, both at
household and national levels. Therefore, improvement of their performances to ensure increased
productivity and adaptability especially in the face of climate change is paramount. In Kenya the
current dairy cattle breeding goal focuses on increased productivity, milk quality, fertility and
functional traits but assume feed efficiency, resilience, adaptability and disease resistant traits.
This is irrespective of existence of negative genotype-environment interaction between large scale
commercial and smallholder dairy farms and these traits being identified by smallholder farmers
to be important due to production constraints and climate change mitigation measure. There is
therefore need to include feed efficiency, resilience, adaptability and disease resistant traits in the
current dairy cattle breeding goal. Their inclusion would require economic values which are
lacking. There is therefore need to estimate their economic values to enable their inclusion in the
breeding goal. Breeding goal with feed efficiency, resilience, adaptability and disease resistant
traits will be robust and therefore can serve both large scale commercial and smallholder dairy
farmers in the face of climate change. It will also minimize the negative impact of genotypeenvironment interaction currently experienced in smallholder dairy farms. Breeding for disease
resistance by including it in the breeding goal would be important for animal welfare and consumer
protection from zoonotic diseases and residues of antibiotics due to treatment in the milk and milk
products. Modelling of breeding program and evaluation of the breeding goal with feed efficiency,
resilience and adaptability traits would provide firsthand information for informed decision
making on implementation of the new breeding goal by taking into account the genotypeenvironment interaction.
1.6 Expected outputs
i. Economic values for feed efficiency, resilience, adaptability and disease resistance
indicator traits derived
ii. Direct genetic and economic gains for breeding goal with feed efficiency, resilience,
adaptability and disease resistance indicator traits in large scale farms estimated
iii. Correlated genetic and economic gains in the smallholder farms when selection is done in
large scale dairy farms based on breeding goal accounting for feed efficiency, resilience,
adaptability and disease resistance indicator traits and genotype-environment interaction
estimated
iv.
At least one paper presented in a conference
v. At least one paper published in a refereed journal
vi.
Master of Science thesis in Animal Breeding and Genomics
5
CHAPTER TWO
LITERATURE REVIEW
2.1 Background of dairy production in Kenya.
In Kenya, livestock farming is an important economic activity due to its role in raising household
incomes, improving food security, providing manure for crop production and providing marketable
products like milk, calves, meat and culling’s (MOA 2009, Technoserve 2008, Karanja, 2003).
Kenya has a vibrant dairy industry that contributes 14% of the agricultural gross domestic product
(GDP), 40% of the livestock sector GDP and 4% of the national GDP. The industry is currently
growing at an average rate of 5–7% per year. It provides employment to over 1.2 million citizens
(KDB 2015).
There are over 1.8 million smallholder milk-producing households who own one to three cows,
which in aggregate own over 80% of the national dairy herd (estimated at 4.2–6.7 million cattle)
(KDB 2015,ILRI 2008). Milk yields of small-scale producers in Kenya are about 5–8 litres per
cow per day, while large-scale farmers typically reach yields of 17–19 litres per cow per day
(Rademaker et al., n.d).The economic vibrancy of the sector is shown in the growth of domestic
milk production, processing capacity, per capita milk consumption and exports as reported in
various reports (KDB 2015, ILRI 2008).
Kenya exports substantial quantities of milk and milk products to the region and intra-regional
trade in dairy products in the East African Community has continued to gain momentum and
benefits the Kenyan dairy industry (MoA 2013). The dairy sector creates employment for many
Kenyans in farms in various parts of the country, milk processing plants, as well as the dairy related
industries (KDB 2014). Through selling of milk, farmers are able to generate income and this has
helped them to raise their living standards. Dairy products contribute towards a healthy nation
since they are rich in proteins, fat, mineral salt and vitamins which are essential for human health.
2.2 Dairy Production Systems in Kenya
2.2.1 Commercial dairy farming
Dairy cattle production in Kenya can be categorized either based on management or commercial
basis. The commercial classification of the dairy cattle production systems is dependent on the
number of animals kept and level of milk production. They include large and smallholder dairy
production systems. The large scale dairy production was mainly practiced by colonialists before
independent Kenya. Currently large-scale dairy production account for 30% of dairy cattle
population and produce 20% of the dairy cattle marketed milk (Njarui et al., 2011). After
independence in 1963, the Kenyan government developed policies that strongly supported the sub6
division and selling of large-scale farms in the highlands at subsidized rates to smallholder farms.
Currently, smallholder farms own 70% of the dairy herds and account for about 80% of the total
produced and marketed milk in Kenya (Njarui et al., 2011; Wambugu et al., 2011, Makino et al.,
2014).
2.2.2 Smallholder dairy production systems in Kenya
The dairy production system adopted in an area depends on human population density and agroecological zones (Staal et al., 2014). In the Kenyan highlands with high population densities, there
is highly intensive smallholder dairy production systems (zero grazing) involving stall feeding
crop residues and planted fodder crops supplemented with concentrates (Njauri et al., 2016). In
these areas, more than three-quarters of the households in the dairy production regions are engaged
in agricultural activities, with 73% practicing integrated crop-dairy production. The households in
the Kenya highlands practicing dairy production, 44%, 33% and 23% practiced zero, semi-zero
and free ranging systems respectively (Bebe et al., 2003).
2.3 Zero, Semi-zero and Free grazing systems
The management category is based on intensity of management level employed by the farmers
(Sagwa et al., 2019). Three production systems in this category include zero, semi-zero and free
grazing system. Zero grazing is predominant in high agricultural potential areas. These areas are
characterized by reliable rainfall throughout the year, high human population growth rate, frequent
land divisions and urbanization. This implies that although pasture for grazing can grow well in
these areas, the grazing land is scarce. The animals are therefore kept in bans or zero grazing units.
This production system therefore demands high input and is labor intensive. The cows are fed on
high quality rations and breeds with genetic high potential for high milk production such as
Holstein-Friesian and Ayrshire are preferred. Milk production per animal under this system has
been demonstrated to be high with herd average production per day of at least 20 litres (Muia et
al., 2011; Wambugu et al., 2011). The semi-zero grazing system partially practices zero and freegrazing. The animals are kept in the pastures but supplemented in the bans or zero grazing units
with formulated rations especially during milking and pastures from cut and carry. This production
system is mainly common among the smallholder dairy farmers in pre-urban areas. Both pure
breeds and their crossbreds with local breeds are kept in this production system. The milk
production per cow, however, is lower compared to zero grazing (Muia et al., 2011; Wambugu et
al., 2011). The cattle are usually Friesian or Ayrshire or their crosses (Kimenchu et al., 2015). An
important element of this system is the use of manure to fertilize food and cash crops, allowing
sustained multiple cropping on small farms. The advantage of this system is; fitting well in the
integrated smallholder production system because there is interdependency and recycling of
resources (Odero-Waitituh et al., 2017). Animals can also be fed according to the level of
production, are easy to manage due to close proximity of the animals and it is easy to control
parasites and infectious diseases (Njarui et al., 2016).
7
The free-grazing systems on the other hand are more pasture based systems. It is the most common
production system practiced by most dairy farmers in Kenya especially the smallholder farmers.
They mainly raise crossbreds and dual purpose breeds such as Sahiwal and Boran (Udo et al.,
2011). Although they produce 80% of the milk produced by dairy cattle and marketed per year,
their production per unit animal is lower (Muia et al., 2011; Wambugu et al., 2011; Wahinya et
al., 2015).
2.4 Stall feeding
The system is practiced in areas of greater land availability, medium to large potential areas where
there are less intensive practices of combined grazing and stall feeding or purely paddock grazing
(Odero-Waitituh et al., 2017). It is characterized by grazing at daytime and stall feeding at night,
the animals are supplemented during milking and farmers keep crosses of the dairy breed of cattle
(Muia et al., 2011).
2.5 Feeds and feeding
The main feeding system is stall-feeding based on cut-and-carry with about 40% of the households
in the smallholder regions offering dairy cattle improved or preserved fodder with supplementation
(Muia et al., 2011). Cattle are fed planted fodder like Napier grass, maize stalks, weeds and crop
residues (Njauri et al., 2016) and sometimes supplemented with concentrate feeds such as grain
millings or compounded dairy feeds (Njauri et al., 2011). It is important to note that in some cases,
a large proportion of fodder is gathered from public or common land or purchased, so feed
resources are by no means limited to those produced on farm (Odero-Waitituh et al., 2017).
According to Njauri et al (2011) approximately 95% of dairy farmers store crop residues for their
livestock but the storage methods were inappropriate to maintain the quality with 93% of the
smallholder farmers experiencing seasonal fluctuation of feed and therefore affecting milk
production.
2.6 Milk production and cost
The estimated milk production is 1300 to 4000 kg per cow per year (Richards et al., 2015). This
depends on the degree of intensification and agro ecological zones, going up to 4575kg/cow/year
in high potential areas(Mugambi & Ngaruiya, 2012). This difference in production is attributed to
the availability of high quality feeds, differences in animal breeds and production systems which
are influenced by agro-ecological zones (Muia et al., 2011). The production per individual animal
was low compared to world’s best of 9000 liters per year (Technoserve 2008). Therefore, there is
potential for a higher production with good management and better feeding practices, since the
genetic potential of Kenyan dairy cattle is far much higher than the milk it produces (MoA, 2016).
Incomes therefore vary with the season, location of the farm, yields achieved, formal and informal
milk sales and the value of by-products such as manure (Odero-Waitituh et al., 2017). This implies
that profits are different at various parts of the country. The intensive zero grazing gives the highest
cost of production because of high cost of factors of production. Smallholder zero-grazing farmers
8
have the highest returns on investment at 40% but the cost of producing a liter increased as it
depends on high level of supplementation with purchased feeds (Kibiego et al., 2015).
Milk processing in Kenya has over the past few years been dominated by four major processors,
namely, the New KCC, Brookside Dairy Limited and Githunguri Dairy Farmers Cooperative and
Sameer Dairies. Each of these companies processes over 100,000 liters per day, with some
processing over 400,000 liters a day during the high season (KDB, 2013).
According to the Kenya Dairy Board (2013), over 40 milk processors have been licensed since the
dairy industry was liberalized in 1992. However, the current number of active milk processing
companies has dropped to 25 as a result of mergers, acquisitions and insolvencies. The national
volumes of milk undergoing processing has grown from 152 million liters in 2001 to 523 million
liters in 2013, an increment of 244% (Kenya Dairy Board, 2014). The output products from the
Kenyan processors include white liquid milk (pasteurized and long life), flavored liquid milk,
fermented milk (yoghurt and cheese), milk powder, cheese, butter, ghee and cream. The Kenyan
milk processors face challenges which include seasonal fluctuations in raw milk supply,
competition from the informal sector and high costs of milk production and processing among
others. There have been increased investments in milk processing in the recent past to meet the
growing demand for quality and safe milk and milk products.
2.7 Milk marketing and Regulation in Kenya
Marketing represents the performance of all business activities involved in the flow of goods and
services from the producer to the consumer. This implies that there are several categories of key
players in the marketing chain each with its own vested interests. The players include consumers,
producers and intermediaries who perform various marketing functions such as transportation or
retailing with the goal of making the highest profit possible. Due to the perishable nature of milk,
it requires quick and efficient marketing for optimum returns. Dairy cooperatives dominate the
marketing of milk in Kenya with most of the marketed milk being produced by small scale farmers
(KDB, 2015).
There has been great emphasis on the organization of small-scale milk producers into groups such
as cooperatives, self-help groups and companies in order to enhance efficiency in marketing of
raw milk through bulking and cooling (KDB, 2017). It is estimated by the Kenya Dairy Board
(2017) that there are approximately 365 groups of this kind who collect, bulk and market the raw
milk to processors, mini dairies, milk bars and traders.
Small-scale milk producers have found it necessary to organize themselves into dairy cooperatives
in order to be able to supply their raw milk to the processing companies and the other market
outlets (Makino et al., 2014).
Marketing of milk to final consumers in Kenya is undertaken through formal and informal
channels. The formal channel is made of licensed operators who include more than 25 processors,
59 mini dairies, 68 cottage industries and 1172 milk bars (Kenya Dairy Board, 2014). The informal
channel is made of itinerant traders who buy milk from the rural producing households and then
9
transport milk in raw form for sale in urban and peri-urban centers where the majority of consumers
are located. More than a decade ago, the informal milk outlets were reported to control 80% of the
marketed milk (Karanja, 2003).
This may not be the current case and the volumes handled by this channel could be much less as
claimed by some of the stakeholders in the industry (KDB, 2017).
Kenya exports substantial quantities of milk and milk products to the region. Intra-regional trade
in dairy products in the East African Community has continued to gain momentum and this
benefits the Kenyan dairy industry. The main products exported are long life milk and milk powder
which earn the country over KSh 1 billion per annum (KDB, 2014).
Kenya's dairy industry is regulated through the Dairy Industry Act, Chapter 336 of the Laws of
Kenya, as enacted in 1958. Under the Act, the Kenya Dairy Board (KDB) was established in order
to "organize, regulate, and develop efficient production, marketing, distribution and supply of
dairy produce in Kenya".
The objective of the regulatory mandate is to ensure the quality and safety of dairy produce and
also fair competition among the operators in the industry. The developmental role aims at
organizing and building the capacity of the stakeholders in the dairy industry to enhance efficiency
and self-regulation. Under promotion, the Board promotes the consumption and markets for
Kenya’s dairy produce in the domestic and export markets.
It has been estimated that about 45 percent of the milk produced is consumed at home by the
household and calves. A FAO study on post-harvest milk losses (food losses) in Kenya noted that
these are highest at the farm level (Muriuki et al., 2003) due to spillage, lack of market and
rejection at market. Milk rejection at market is partly due to poor handling and the time taken to
reach markets (long distances and bad roads). Losses at the farm level can be more than 6% of
total production, which means that at current production levels, national annual losses may reach
over 0.60 million tonnes (FAO, 2011).
It is estimated that about 85% of marketed milk is sold raw. However, the Kenya Dairy Board
(KDB) (2017), the Ministry of Health and the Kenya National Bureau of Statistics and others in
the formal milk trade have claimed that the proportion of raw milk being preferred to are as
follows:
• It is 20 to 50 percent cheaper than processed milk.
• Some people prefer the taste and high butterfat content of raw milk.
• Raw milk is sold in variable quantities, depending on how much money the customer has to
spend.
• It is widely accessible and within the reach of many people.
The selling of milk through the unprocessed channel is of concern because of the perceived health
risk, particularly owing to its microbial load by the time it reaches the consumer (FAO, 2011).
2.8 Smallholder dairy breeding program in Kenya.
10
2.8.1 Background information
Smallholder dairy farming, characterized by small herds of 2–3 milking cows, provides a
livelihood for more than 150 million farm households worldwide (FAO 2010; DGEA 2015). The
major stimulator of the growth in smallholder dairying is the recent increase in demand for fresh
milk and other value-added milk products triggered by a growing population (Gillespie & Bold,
2017). This demand is replicated in all regions of sub-Saharan Africa (World bank, 2011).
However, per animal productivity is still low mainly because of using inappropriate genetics, poor
husbandry practices, and feed scarcity. With the world population projection to hit 9.15 billion in
2050 (UNDP, 2008), the unit productivity of the animals must be increased. As such, use of
modern breeding technologies and best management practices for more effective production is of
critical importance if this demand is to be met (Chawala, 2019). Increasing milk yield per cow as
opposed to the number of animals would be ideal for proper utilization of available feed resources
(Morotti et al., 2016). In light of the above, strategies to increase productive performance of
animals must be in place specifically targeting small-scale farmers who make up majority of dairy
farms in developing countries (Richards et al., 2015). The International Livestock Research
Institute has recently conducted participatory studies—mainly surveys in smallholder dairy
production systems—as part of various projects, such as Dairy Genetics East Africa, East Africa
Dairy Development and More Milk-IT, to identify the important traits that farmers consider when
selecting dairy cattle (DGEA, 2015). Ndumu et al. (2008) suggested the use of a combination of
survey, ranking and choice experiment methods when identifying traits for selection. In trait
preference ranking studies, surveys and trait ranking methods are used to collect information at an
early stage, with the aim of obtaining a general picture of the list of traits to be considered in a
breeding objective. The choice experiment method has been widely used for quantifying farmers’
preference traits for various livestock species, including cattle (Ndumu et al., 2008), sheep (Ragkos
& Abas, 2015) and pigs (Roessler et al., 2009). Famers are willing to keep a cow with high milk
yield, good fertility, easy temperament, low feed requirement and high tropical disease resistance,
in order of importance (Chawala, 2019).
2.8.2 Breeding goal
Development of breeding goal is the first step in genetic improvement as it defines the direction
of selection and genetic merits of performance traits(Wolc et al., 2011; Åby et al., 2012). It
involves, (a) identification of the breeding, production and marketing systems; (b) identification
of sources of income and expenditure; (c) determination of biological traits influencing revenues
and costs; and (d) derivation of economic values for each trait in the breeding goal.
a) Specification of breeding, production and marketing systems: Breeding system to be
utilized helps in identification of breed and genotypes to be raised (Ponzoni, Newman, &
Ponzoni, 2014). Different dairy breeds are raised in Kenya mainly for milk production
(Sagwa et al., 2019). There is also need for the understanding of the production system in
which animals are to be raised; husbandry, herd composition, age and replacement policy.
11
The production and marketing systems account for inputs and outputs and their importance
to producers and consumers. Marketing systems can be; farm gate, tertiary and terminal.
b) Identification of sources of incomes and expenses: Actual sources of costs and incomes
have to be identified. This is important for computation of profitability in a production
system. The major sources of income in dairy production include; whole milk, milk
products such as cheese, ghee, ice cream and yoghurt, heifers and culled cows and bull
calves. The sources of costs include; feeding, veterinary services, labor and marketing costs
(Kahi & Nitter, 2004).
c) Determination of biological traits influencing revenues and costs: Biological traits
influencing revenues and costs in dairy production system in Kenya have been identified
(Kahi & Nitter, 2004). The traits in the breeding goal include: milk yield, fat yield, age at
first calving, calving interval, average daily gain, pre-weaning daily gain, live weight, preweaning survival rate, post-weaning survival rate and cow productive lifetime. The
breeding goal however ignored milk quality traits mainly because milk is marketed in terms
of volume and no restriction on milk quality traits (Sagwa et al., 2019). Milk quality traits
include; fat, protein, ash among others and proportion in milk varies from breed, season,
diet and parity (Glantz et al., 2009). The current milk marketing in Kenya is shifting
towards quality hence milk quality traits need to be included in breeding goal (Sagwa et
al., 2019).
d) Derivation of economic values for each trait in the breeding goal: Economic value is the
change in profitability of a production system due to a unit change in genetic gain of a
given trait while the other traits in the breeding goal remain constant (Groen, 2015).
Different methods have been used to derive economic values. They include analysis of
field data and bio-economic models (Kahi & Nitter, 2004; (Sölkner et al., 2008). Deriving
economic values based on field data is not common because it uses historical prices while
breeding is future oriented (Sagwa et al., 2019). Most studies therefore have derived
economic values using bio-economic models. In this model, economic values can be
estimated using either simple or risk rated models (Kulak et al., 2003; Mbuthia et al., 2014);
Wahinya et al., 2015). Simple profit models have been demonstrated to overestimate
economic values because it assumes perfect knowledge of all relevant parameters and
constant economic circumstances (Kulak et al., 2003; (Okeno et al., 2012)). On the other
hand, risk-rated models account for imperfect knowledge concerning risk attitude of
producers and variance of input and output prices (Kulak et al., 2003; Okeno et al., 2012;
Mbuthia et al., 2015). In Kenya, the economic values of traits in the breeding goal have
been derived using simple bio-economic model (Kahi & Nitter, 2004; Sagwa et al., 2019).
They are presented in Table 1. These economic values reflect the production and economic
environment under which the dairy cattle are raised in Kenya and account for farmers,
marketers and consumer needs.
12
Table 1. Economic values in Kenya shillings (1USD$=KES 107.783) for traits in the breeding
objective under two payment systems for milk.
Source
Payment systems
Milk volume and fat
Traits
Milk yield(Kg)
16.05
Fat yield (Kg)
79.44
Protein yield (Kg)
779
Mastitis Resistance (cells/ml)
-2364
Age at first calving(days)
-2.72
Calving interval(days)
2.65
Pre-weaning daily gain (%)
1.04
Post-weaning daily gain (%)
3.4
Live weight(Kg)
7.95
Pre-weaning survival rate (%)
9.96
Post-weaning survival rate (%)
45.15
Productive Lifetime
0.07
Source: Kahi & Nitter, 2004 and Sagwa et al., (2019)
Milk volume
18.03
-2.76
_
_
-2.72
2.65
1.04
3.4
7.95
9.96
45.15
0.07
2.9.1 Computer simulations in animal breeding
Simulation is the process of data generation using computer models to mimic reality based on the
initial input realistic data. Three computer simulation models have been used to model and evaluate
breeding programs. They include stochastic, deterministic and pseudo-stochastic (Rutten & Bijma,
2002);(Roessler et al., 2009; Pedersen et al., n.d.). The deterministic models assume that the input
parameters are constant throughout the simulation period (William et al., 2008) while in stochastic
models the parameters change with new generated information and therefore different runs yield
different results (Pedersen et al., 2009). Pseudo-stochastic model is a combination of both
deterministic and stochastic models. This implies that part of the input parameters do not change
with time while others are adjusted based on the new data generated over time (Rutten et al., 2002).
Although stochastic models reflect true case scenarios as input parameters always change with
time under natural circumstances, deterministic model will be used in this study.
2.9.2 Bio-economic modelling
The concept of bio-economic models refers to the use of mathematical techniques to model the
performance of ‘living’ production systems subject to economic, biological and technical
constraints (Allen et al., 1984). Bio-economic models address the systematic integration of
biological performance and physical systems and relate them to economic considerations, which
13
include market prices, resource allocation and institutional constraints (Cacho, 2000). Bioeconomic modelling provides an alternative method to represent the production process as
compared to conventional production function analysis. It allows evaluations of a wider range of
environmental conditions than would be normally possible with purely economic models, since
biotechnical relationships can be more clearly defined (Herna, 2002). Bio-economic models
therefore, are a good methodological approach to study the interaction of the various components
(biological, physical, technological, economic, and institutional) of dairy production systems. Bioeconomic models can provide answers to the questions of economic feasibility, optimal system
design, optimal methods of operations, and research direction (Herna, 2002).
Bio-economic models can be used to assist producers and decision-makers in identifying optimal
production system designs and operation management approaches and alternative development
and policy strategies.
2.9.3 Conventional and Genomic breeding programs
Conventional selection program (CS) uses pedigree as the connection between relations. It requires
phenotypes and Progeny Testing (PT) hence long generation intervals.
Genomic selection (GS), originated by Meuwissen et al. (2001) and successfully applied in dairy
cow breeding (Hans Dieter Daetwyler, n.d.), has gained much attention among plant
geneticists/biostatisticians. GS refers to selection based on predictions from DNA markers densely
covering the whole genome, for traits that breeders normally select (Yan, 2019). Key techniques
for applying GS in animal breeding are largely in place, including new marker technologies such
as genotyping by sequencing (Kim et al., 2019), bioinformatics tools for handling and analyzing
massive sets of markers (Tinker et al., 2016; Bekele et al., 2018), and sophisticated phenotypegenotype modeling methods (Goddard, 2009; Nakaya & Isobe, 2012; Desta & Ortiz, 2014). The
major benefit of this method of selection over pedigree based method is increase in the accuracy
of estimated breeding values and response to selection and use in sex limited traits and the ones
measured late in life (Avendaño et al., 2011; Fulton et al., 2016). Genomic selection results to
reduction in the rate of inbreeding between individual because of the ability of the markers to
generate information on Mendelian sampling terms (Daetwyler et al., 2007). This reduces the
emphasis placed on family information and therefore reduction of correlations of Estimated
Breeding Values among family members and co-selection of relatives (Jembere et al., 2017). The
application of genomic selection requires genetic and phenotypic parameter estimates. Genomic
selection facilitates early selection, as early as birth.
2.9.3 Reproductive Technologies in dairy cattle breeding (AI, MOET)
Reproductive technologies are technologies geared towards increasing; selection intensities,
accuracy of Estimated Breeding Values (EBVs) and decreasing generation intervals.
a) Artificial Insemination (AI)
14
Artificial insemination is a breeding technique where there is more intensive use of best sires. AI
has made it possible the use of overseas bulls, to establish links between herds and Progeny Testing
(PT) which has ultimately accelerated rapid dissemination of superior genetics. AI has contributed
to development of improved breeds, in so far as breeding animals used for insemination have
genetic value known to the breeder (Malafosse, 1990). AI has resulted in genetic differences
between breeding animals of the same breed. Consequently, selection schemes have been
developed for each breed, taking into account the individual value of each breeding animal.
b) Multiple Ovulation Embryo Transfer (MOET)
Multiple Ovulation Reproductive Transfer is a reproductive technology in which intensive use of
best cows is practiced. This technology has made it possible the use of overseas cows, increased
selection intensity, reduced generation interval and increased accuracy of Estimated Breeding
Values (EBVs) (Malafosse, 1990). This ultimately increases long term genetic gains in animal
breeding programs
15
CHAPTER THREE
3.0 MATERIALS AND METHODS
3.1 Procedure
Computer simulation approach will be used in this proposed study derive economic values,
model breeding schemes and predict response to selection for the breeding goal with feed
efficiency, resilience, adaptability and disease resistance indicator traits. Simulation is the process
of data generation using a computer models to mimic reality based on the initial realistic input
data. Three computer simulation models have been used to model and evaluate breeding programs.
They include stochastic, deterministic and pseudo-stochastic (Rutten et al., 2002; Willam et al.,
2008; Pedersen et al., n.d.). The deterministic models assume that the input parameters are constant
throughout the simulation period (Willam et al., 2008) while in stochastic models the parameters
changes with new generated information and therefore different runs yield different results
(Sørensen et al., 2014). Pseudo-stochastic model is a combination of both deterministic and
stochastic models. This implies that part of the input parameters do not change with time while
others are adjusted based on the new data generated over time (Rutten et al., 2002). Although
stochastic models reflect true case scenarios as input parameters always change with time under
natural circumstances, deterministic model will be used in the current study. This is because of
availability of the software needed to carry out this study. The bio-economic model and selection
index (Hazel, 1943) will be used to derive economic values for feed efficiency, resilience,
adaptability and disease resistance indicator traits, respectively, while ZPLAN+ (Willam et al.,
2008) will be adopted to model and evaluate response to selection for the modeled breeding
schemes. Data on dairy cattle management and production systems in Kenya will be sampled from
previous studies carried in Kenya. Where such information is lacking under Kenyan production
conditions, other tropical studies will be consulted.
3.2 Breeding goal
The breeding goal for dairy cattle production in Kenya has been defined (Kahi & Nitter,
2004) and reviewed (Wahinya et al., 2015; Sagwa et al., 2019). This breeding goal is market
oriented and strives at producing dairy cattle with high milk production under the Kenyan
production conditions. The breeding goal also accounts for functional and milk quality traits
(Wahinya et al., 2015; Sagwa et al., 2019). The economic values of traits in the breeding goal were
objectively estimated based on change in profitability of the production system due to a unit change
in one trait holding other traits constant. The traits in the breeding goal included milk yield (MY),
fat yield (FY), age at first calving (AFC), calving interval (CI), pre-weaning daily gain (DG), postweaning daily gain to 18 months (PDG), live weight (LW), pre-weaning survival rate (PreSR),
post-weaning survival rate (PostSR), productive life time (PLT), protein yield (PY) and mastitis
resistance (MR)(Kahi, Nitter, 2004; Sagwa et al., 2020). The economic values of these traits were
estimated under fixed herd and pasture production systems. In each production system the
economic values were estimated when price of milk was based either volume, fat content or protein
16
yield. In this study, the economic values estimated under fixed herd production system will be
adopted after adjustments to reflect the current market inflation rates. This is because; in Kenya
different factors such as land size, labor, management skills and availability of feeds determine
herd size. The adjustment of the economic values will be necessary because the market is dynamic
and input and output prices change overtime depending on inflation rates. These economic values
therefore will be adjusted by multiplying them by their respective cumulative discounted
expressions (CDE). The CDE reflects time and frequency of future expression of a trait in a
superior genotype from selected parents ((Berry et al., 2006; Nishio et al., 2008). The economic
values for the traits in the current breeding goal are presented in Table 2 (Kahi and Nitter 2004;
Sagwa et al., 2020).
3.3 Derivation economic values for feed efficiency, resilience, adaptability and disease
resistance indicator traits
The bio-economic modelling will be used to derive economic values for feed efficiency, resilience
and adaptability indicator traits in the breeding goal. Bio-economic model is the systematic
integration of biological performance and physical systems and relate them to economic
considerations, which include market prices, resource allocation and institutional constraints. The
bio-economic model will be developed in Python computer program. The program will account
for all the traits in the current breeding goal and new traits to be included such as feed efficiency,
resilience, adaptability and disease traits. Feed efficiency will be measured by Net Energy
requirement for lactation as described by (Agabriel et al., 2020). While in growing animals the
Kleiber ratio (KR; Kleiber, 1961) which is the ratio between the average daily gain and metabolic
body weight will be used. Animals with higher values of KR will be considered to be desirable for
feed efficiency trait.
The variance deviations (δ2) of milk yield before and after disturbance (drought) will be used as
an indicator trait for resilience. Animals with small variance deviations (δ2) will be considered
resilient than those with larger variance. Heat stress on the other hand will be used as an indicator
for adaptability trait. Temperature Humidity Index (THI) (Eigenberg et al., 2005) will be used an
inference point on heat stress.
Disease resistance: The economic value for disease resistance traits cannot be estimated using
profit equations because they have multifold effects on input and output which in turn affects
profitability (Sivarajasingam, 1995). Nielsen, (2005) described a method for estimating economic
value for mastitis resistance based on selection index methodology (Hazel, 1943). This method
matches the breeding objective to expected responses in production traits and responses in these
traits are maximized relative to overall gains. This methodology will be adopted in the current
study to estimate economic value for disease resistance. Brucellosis disease will be used as an
indicator for disease resistance. Brucellosis is a zoonotic disease highly prevalent in dairy cows in
17
Kenya and cause losses to dairy farmers through abortion at the end of gestation period, reduction
in milk yield and infertility (Musallam et al., 2019). It is a bacterial disease caused by Brucella
abortus. The economic value for Brucellosis resistance will be estimated relative to MY. The
number of colony forming units from bacterial culture of milk will be used as an indicator trait for
Brucellosis disease. Animals with detection threshold of >30 CFU ml-1 of milk are considered to
be infected with Brucellosis (Martínez et al., 2010).
Risk rated profit function as described by Kulak et al. (2003) will be used to compute economic
values. This is because most of smallholder dairy farmers in Kenya are risk-averse (Bett et al.,
2012). The Arrow Pratt coefficient of absolute risk aversion of 0.02 will be adopted (Kulak et al.,
2003). The risk rated economic values (EV) will be computed as
𝐸𝑉 =
∆𝜋
(1)
∆𝑔
where ∆π and Δg are marginal changes in risk-rated profits and breeding value of a trait
respectively, after an increase in the breeding value of a trait of interest by one unit. The risk-rated
profit (π) will be computed as:
𝜋 = 𝜖 − 0.5𝜆𝛿𝜖2
(2)
where 𝜖 is the expected profit, 𝜆 the Arrow-Pratt coefficient of absolute risk aversion and 𝛿𝜖2 the
variance of the profit due to variability in input and output prices (Kulak et al.,2003; Okeno et al.,
2012; Mbuthia et al., 2015).
The 𝜖 will be estimated as
𝜖 = 𝜇𝑃𝑜 ∫(𝑔,𝑒)−𝑒𝜇𝑝𝑖
(3)
where μpo and μpi will be the expected values of output and input prices respectively, g, the vector
of the variables determined by the genotype and e the vector of the variables associated with inputs.
3.4 Estimation of direct response to selection for breeding goal accounting for feed efficiency,
resilience, adaptability and disease resistance indicator traits in large scale dairy farms in
Kenya
The rates of genetic and economic returns per cow per year will be computed through deterministic
simulation approach. Computer program ZPLAN+ will be used to model and evaluate a two-tier
closed nucleus breeding scheme that mimics the current breeding program of dairy cattle in Kenya
(Sagwa et al., 2019). In Kenya, the large scale commercial dairy farms represent nucleus as they
are responsible for producing the breeding stock used in smallholder farms. The smallholder farms,
on the other hand represent the commercial production units as they rarely produce their
replacement stock but source them from large scale commercial dairy farms (Bebe et al., 2002).
In this breeding program, two breeding goals will be considered. Firstly, the current dairy cattle
breeding goal in Kenya where the focus is more on production, product quality, reproduction and
functional traits. Secondly, an alternative breeding goal which will be similar to the current goal
but with additional traits on feed efficiency, resilience, adaptability and disease resistant indicator
18
traits. The aim of the alterative goal is to be breed for robust dairy cattle that can survive under
smallholder production systems even with effects of climate change.
During modeling a simulated dairy cattle population of 6.7 million cows will be assumed and
distributed in the two tiers. The top ranking 20% will be in the nucleus (large scale commercial
dairy farms) while the remaining 80% will constitute the commercial farms (smallholder farms).
This is consistent with distribution of dairy cattle population in Kenya between large scale
commercial and smallholder farmers (KNBS, 2017). Truncation selection based on true breeding
values will be used to select top ranking bulls and cows for breeding in the nucleus. The second
top ranking bulls and cows will be used for breeding in the lower tier based on the breeding system
adopted. Candidates not selected for breeding in the nucleus and commercial populations will be
culled and sold for meat production. In all the systems only genomic selected bulls will be used to
disseminate genetic gain realized in the nucleus to the commercial population through artificial
insemination (AI). This is because of two reasons. Firstly, Sagwa et al. (2019) recommended the
use of genomic selection with AI in dairy cattle in Kenya due to high response to selection realized.
Secondly, most of the semen used for AI in dairy cattle in Kenya currently are from genomic tested
bulls (KAGRC, 2018). Different selection pathways will be considered in disseminating of genetic
gain. The main pathways will include: sires to breed sires, sires to breed dams, dams to breed sires
and dams to breed dams. Each selection group will have different sources of information for
different traits in the breeding goal.
The heritability, phenotypic standard deviations, genetic and phenotypic corrections of traits in the
breeding goal are some of the input parameters required when modeling breeding programs. These
input parameters should be population specific. They can, however, be sourced from studies of
other populations if unavailable in the study population (Kariuki et al., 2017). In the current study,
these parameters will be sourced from studies conducted in Kenya (Kahi and Nitter 2004;Ilatsia et
al., 2011; Wahinya et al., 2015; Sagwa et al., 2019). Where some parameters are missing, they
will be sourced from other studies in the tropics and subjected to meta-analysis to minimize
biasness attributed to estimating using different models, data sizes and geographical regions
(Jembere et al., 2017). The economic values for the traits in the breeding goal will be sourced of
objective 1 of the current study.
The response to selection for all traits in the breeding goal (H) will be computed as the sum of the
true breeding values (TBV) of each traits in the breeding goal a weighted its economic value. A
selection index, which is the sum of the TBVs for the traits in the breeding goal and their economic
values, will be computed as (Williams et al., 2011):
𝐻 = 𝐴1 𝑉1 + 𝐴2 𝑉2 + ⋯
(4)
where A are true breeding values, and V are the economic values for each trait in the breeding goal
19
To account for genomic selection, genomic traits will be included in the selection index as extra
correlated traits with heritability equal to one (Dekkers, 2007). The genetic and phenotypic
correlations between the true and the extra trait will be calculated as ℎ𝑟𝑔𝑔 and 𝑟𝑔𝑔 , where ℎ is the
square root of the heritability of the trait and 𝑟𝑔𝑔 the accuracy of the genomic true breeding values.
The 𝑟𝑔𝑔 will be determined by the size of the reference population (𝑛𝑝 ), the effective number of
loci in the base population (𝑛𝐺 ), and the correlation of the true breeding values of the genotyped
individuals and their phenotypes (r), and will be computed as described by (Van Grevenhof et al.,
2012).
𝜆𝑟 2
𝑟𝑔𝑔 = √𝜆𝑟 2
where,  
(5)
np
nG
2
and r is the heritability, nG  2 N E L ,where N E is the historic effective size of the
base population and L is the size of the genome in Morgan. The genome (L) of dairy cattle will be
assumed to be 30 Morgan units (Kariuki et al., 2017), and effective population size of 156 (Muasya
et al., 2013). The genetic and phenotypic correlations between the genomic traits will be computed
based on the procedure described by Dekkers (2007).
The genetic gain for traits in the breeding goal will be computed as;
∆𝑮 = 𝒃′ 𝑮𝒊𝙄𝝈𝙄
(6)
where ∆𝑮 is a vector containing selection response for each trait; 𝒃 is a vector of index weights
and 𝑮, is a matrix of co-variances between information sources and true breeding values of
selection candidates, i, the selection intensity and 𝜎𝘐 , the standard deviation of the index. The total
gain in the breeding goal in economic units will be computed as;
∆𝐻 = 𝑖𝜎𝐼
(7)
where ∆H is the breeding goal.
The of rate of genetic gain for each cow will be predicted as linear regression of true breeding
values for each trait in the breeding goal weighted by its corresponding economic values and
expressed per year.
The annual economic returns per cow will be computed as based on profitably per cow in each
breeding system. The profitability per cow will be estimated as:
 Rt  ct
t
t  0  1  r 
T
   



(8)
where T is the evaluation period (25 years), Rt the annual benefits of genetic improvement
calculated as realized genetic gain per cow per year, ct the costs of genetic improvement which
includes fixed and variable costs and r the discounting rate. The discounting rate of 5% has been
20
recommended when evaluating animal breeding programs (Bird and Mitchel, 1980; Keeper, 1978)
and it will be adopted in the current study.
3.5 Estimation of correlated response to selection for breeding goal in smallholder dairy
farms when selection is done in large scale dairy farms based on breeding goal accounting
for feed efficiency, resilience, adaptability and disease resistance while considering genotypeenvironment interaction between the two populations
The modeling procedures to compute response to selection for traits in the breeding goal will be
similar that presented in Objective 2. In this objective, however, the effect of genotypeenvironment interaction between the nucleus (large scale commercial farms) and smallholder
farms will be accounted for. This will be achieved by computing for expected response to selection
in smallholder dairy farms assuming selection is done in the large scale commercial dairy farms.
The correlated response to selection of traits in the breeding goal, CRY in the smallholder farms to
direct selection in the large scale farms will be calculated as (Falconer, 1999);
𝐶𝑅𝒀 = 𝒊 𝒉𝒍 𝒉𝒔 𝒓𝒈 𝜹
(9)
𝒑𝒍
where, i is the intensity of selection of trait in the large scale farms, hl and hs are accuracies of
selection in large scale commercial and smallholder production environments, respectively. rg is
the regression coefficient between the two environments. δpl is phenotypic standard deviation of a
traits in the large scale environment. The genetic correlation between the two production
environments will be obtained from Muasya et al. (2014).
3.6 Data analysis
A deterministic computer program for simulating livestock breeding programs, ZPLAN+
(Willan et al., 2008) will be used to model and evaluate the breeding systems. Using the gene flow
methods and selection index procedures, the ZPLAN+ simulates different breeding plans in any
livestock species. It computes genetic gain for the aggregate breeding value, the annual response
for each selection and correlated trait defined and the profit per female animal in the population
by subtracting breeding costs from returns. The program uses genetic, biological and economic
parameters provided in the input files to calculate the costs and returns. The calculations assume
that the input parameters and the defined selection strategies remain unchanged over the
investment period with one round of selection. Reduction in genetic variance and change in rate
of inbreeding is, however, not considered. Further, the program applies order statistics to obtain
adjusted selection intensities for population with finite sizes. ZPLAN+ has been widely used to
model and evaluate cattle breeding programs such as dairy cattle and goats (Sagwa et al., 2019;
Gore et al., 2021).
21
4.0 WORKPLAN
YEAR
ACTIVITY
2021
2022
MAR APRIL MAY JUN JUL AUG SEPT OCT NOV DEC JAN FEB MAR APR
Literature
Review
proposal defense
Data Collection
Estimation of
EVs
Calculation of
response to
selection
Response to
selection
comparison
Thesis writing
and submission
22
5.0 BUDGET
Items
Proposal Writing
Printing
Photocopying
Binding
Sub-Total
Research
ZPLAN+ Computer program
Sub-Total
Thesis writing
Printing
Photocopying
Binding
Printing 800 copies for hard cover binding
Hard cover binding
Sub-Total
Conference and Publication
Registration
Journal publication
Sub-Total
Quantity
Unit Cost (KES)
Total (KES)
30.0
90.0
4.0
10.00
3.00
100.00
300.00
270.00
400.00
970.00
1.0
330,000.00
330,000.00
330,000.00
100.0
700.0
8.0
800.0
8.0
10.00
3.00
100.00
10.00
500.00
1,000.00
2,100.00
800.00
8,000.00
4,000.00
15,900.00
1.0
2.0
20,000.00
30,000.00
20,000.00
60,000.00
80,000.00
Total costs
Remarks
Printing 30 pages @KES 10.00
Photocopying 3 copies of 30 pages
Spiral binding of 4 copies of proposal
Program license
Printing 100 paged thesis
Photocopying 7 copies of 100 paged thesis
Spiral binding of 8 copies of thesis
Printing 8 copies of 100 paged thesis
Eight Hardbound copies of thesis
426,870.00
Source of funding: The project will be partially funded by Climate Smart Research and
Innovation in Livestock Development with focus on Dairying in Kenya
23
6.0 REFERENCES
A, I. G. C., & A, B. C. H. (2016). Resilience in farm animals : biology , management , breeding
and implications for animal welfare. 1961–1983.
Åby, B. A., Aass, L., Sehested, E., & Vangen, O. (2012). A bio-economic model for calculating
economic values of traits for intensive and extensive beef cattle breeds. Livestock Science,
143(2–3), 259–269. https://doi.org/10.1016/j.livsci.2011.10.003
Agabriel, J., Agabriel, J., Alimentation, D., & Productions, A. (2020). Dossier ” Alimentation
des Ruminants ” - Avant-propos To cite this version : HAL Id : hal-02653663
ALIMENTATION DES RUMINANTS Avant-propos. 20(2), 107–108.
Al-qaisi, K. M. (2011). The Economic Determinants of Systematic Risk in the Jordanian Capital
Market. 2(20), 85–95.
Avendaño, C., Lafitte, T., Galindo, A., Adjiman, C. S., & Müller, E. A. (n.d.). SAFT- γ force
field for the simulation of molecular fluids : I . A single-site coarse grained model of carbon
dioxide. 1–60.
Bebe, B. O., Udo, H. M. J., Rowlands, G. J., & Thorpe, W. (2003). S mallholder dairy systems in
the Kenya highlands : breed preferences and breeding practices. 82, 117–127.
Bebe, B. O., Udo, H. M. J., & Thorpe, W. (2002). Development of smallholder dairy systems in
the Kenya highlands. 31(2), 113–120.
Bekele, W. A., Wight, C. P., Chao, S., Howarth, C. J., & Tinker, N. A. (2018). Haplotype-based
genotyping-by-sequencing in oat genome research. 1452–1463.
https://doi.org/10.1111/pbi.12888
Bekman, H., & Arendonk, J. A. M. Van. (1993). Derivation of economic values for veal , beef
and milk production traits using profit equations. 34, 35–56.
Berry, D. P., Authority, F. D., Madalena, F. E., & Amer, P. (n.d.). Cumulative Discounted
Expressions of Dairy and Beef Traits in 2. (February 2014).
Bett, R. C., Gicheha, M. G., Kosgey, I. S., Kahi, A. K., & Peters, K. J. (2012). Economic values
for disease resistance traits in dairy goat production systems in Kenya Economic values for
disease resistance traits in dairy goat production systems in Kenya. Small Ruminant
Research, 102(2–3), 135–141. https://doi.org/10.1016/j.smallrumres.2011.07.008
Chawala, A. R. (2019). Farmer-preferred traits in smallholder dairy farming systems in
Tanzania. 1337–1344.
Daetwyler, H D, Villanueva, B., Bijma, P., & Woolliams, J. A. (2007). Inbreeding in genomewide selection. 124, 369–376.
Daetwyler, Hans Dieter. (n.d.). No Title.
Dekkers, J. C. M. (2007). Prediction of response to marker-assisted and genomic selection using
selection index theory. 124(1961), 331–341.
Desta, Z. A., & Ortiz, R. (2014). Genomic selection : genome-wide prediction in plant
improvement. Trends in Plant Science, 19(9), 592–601.
https://doi.org/10.1016/j.tplants.2014.05.006
Dikmen, S., & Hansen, P. J. (2009). Is the temperature-humidity index the best indicator of heat
24
stress in lactating dairy cows in a subtropical environment ? Journal of Dairy Science,
92(1), 109–116. https://doi.org/10.3168/jds.2008-1370
Faculty, U. U. N. L., Eigenberg, R. A., Nienaber, J. A., & Hahn, G. L. (2005). DigitalCommons
@ University of Nebraska - Lincoln Dynamic Response Indicators of Heat Stress in Shaded
and Non- shaded Feedlot Cattle , Part 2 : Predictive Relationships Dynamic Response
Indicators of Heat Stress in Shaded and Non-shaded Feedlot.
https://doi.org/10.1016/j.biosystemseng.2005.02.001
Falconer, D. J. (1999). ONTOLOGICAL PROBLEMS OF PLURALIST RESEARCH.
(January).
Ferrell, C. L. (1985). COW TYPE A N D THE N U T R I T I O N A L E N V I R O N M E N T :
NUTR I T I O N A L ASPECTS 1 FERRELL AND JENKINS 61(3).
Fulton, J. E., Sullivan, O., Avendano, A., Watson, K. A., Hickey, J. M., Campos, G. D. L., &
Fernando, R. L. (2016). Implementation of genomic selection in the poultry industry. 6(1),
23–31. https://doi.org/10.2527/af.2016-0004
Garc, A. (2012). Understanding and Predicting the Fitness Decline Mutation , and Standard
Selection. 190(April), 1461–1476. https://doi.org/10.1534/genetics.111.135541
Genetic, A. (2013). Animal genetic resources.
Gillespie, S., & Bold, M. Van Den. (2017). Agriculture , Food Systems , and Nutrition : Meeting
the Challenge. https://doi.org/10.1002/gch2.201600002
Glantz, M., Månsson, H. L., Stålhammar, H., Bårström, L., Fröjelin, M., & Knutsson, A. (2009).
Effects of animal selection on milk composition and processability. Journal of Dairy
Science, 92(9), 4589–4603. https://doi.org/10.3168/jds.2008-1506
Goddard, M. (2009). Genomic selection : prediction of accuracy and maximisation of long term
response. 245–257. https://doi.org/10.1007/s10709-008-9308-0
Gore, D. L. M., Okeno, T. O., Muasya, T. K., & Mburu, J. N. (2021). Improved response to
selection in dairy goat breeding programme through reproductive technology and genomic
selection in the tropics. Small Ruminant Research, 200(August 2020), 106397.
https://doi.org/10.1016/j.smallrumres.2021.106397
Groen, A. F. (2015). Production : Influences of production circumstances on the economic
revenue of cattle breeding programmes INFLUENCES OF PRODUCTION
CIRCUMSTANCES ON THE. (September 2010), 469–480.
https://doi.org/10.1017/S0003356100012502
Haile, G. G., & Tang, Q. (2020). Projected Impacts of Climate Change on Drought Patterns Over
East Africa Earth ’ s Future. 1–23. https://doi.org/10.1029/2020EF001502
Hazel, L. N. (1943). The genetic basis for constructing selection i n d e x e s. (November), 476–
490.
Herna, J. M. (2002). Bioeconomic analysis of production location of sea bream ( Sparus aurata )
cultivation. 213, 219–232.
Ilatsia, E. D. A., Roessler, R. A., Kahi, A. K. D., Piepho, H. B., & A, A. V. Z. (2011). Evaluation
of basic and alternative breeding programs for Sahiwal cattle genetic resources in Kenya.
25
682–694.
Jembere, T., Dessie, T., Rischkowsky, B., Kebede, K., Okeyo, A. M., & Haile, A. (2017). Metaanalysis of average estimates of genetic parameters for growth , reproduction and milk
production traits in goats Meta-analysis of average estimates of genetic parameters for
growth , reproduction and milk production traits in goats. Small Ruminant Research,
153(May), 71–80. https://doi.org/10.1016/j.smallrumres.2017.04.024
Jkuat, T., & Ngaruiya, P. M. (2012). STRATEGIC AND VALUE CHAIN STUDY OF THE
SMALLHOLDER DAIRY SECTOR IN CENTRAL KENYA. (March).
Kahi, A. K., & Nitter, G. (2004). Developing breeding schemes for pasture based dairy
production systems in Kenya I . Derivation of economic values using profit functions. 88,
161–177. https://doi.org/10.1016/j.livprodsci.2003.10.008
Kahi, A. K., Nitter, G., & Gall, C. F. (2004). Developing breeding schemes for pasture based
dairy production systems in Kenya II . Evaluation of alternative objectives and schemes
using a two-tier open nucleus and young bull system. 88, 179–192.
https://doi.org/10.1016/j.livprodsci.2003.07.015
Karanja, A. M. (2003). THE DAIRY INDUSTRY IN KENYA : THE POSTLIBERALIZATION By Table of Contents. 2003(August 2002).
Kariuki, C. M., Brascamp, E. W., Komen, H., Kahi, A. K., & Arendonk, J. A. M. Van. (2017).
Economic evaluation of progeny-testing and genomic selection schemes for small-sized
nucleus dairy cattle breeding programs in developing countries. Journal of Dairy Science,
1–11. https://doi.org/10.3168/jds.2016-11816
Keeper, S. (1978). Tahitian DiuI ’ Loties at San Diego Zoo.
Kibiego, M. B. (2015). Competitiveness of Smallholder Milk Production Systems in Uasin Gishu
County of Kenya. 6(10), 39–46.
Kim, C., Guo, H., Kong, W., Chandnani, R., Shuang, L., Andrew, H., & Paterson, A. H. (2019).
Application of genotyping by sequencing technology to a variety of crop breeding
programs. 1–30.
Kimenchu, D., Mwangi, M., Kairu, S., & M, G. A. M. (2015). Assessment of performance of
smallholder dairy farms in Kenya : an econometric approach. 7891–7899.
Kulak, K., Wilton, J., Fox, G., & Dekkers, J. (2003). C omparisons of economic values with and
without risk for livestock trait improvement. 79, 183–191.
Lukuyu, M. N., Gibson, J. P., Savage, D. B., Rao, E. J. O., Ndiwa, N., Duncan, A. J., … Group,
F. (2019). Farmers ’ Perceptions of Dairy Cattle Breeds , Breeding and Feeding Strategies :
A Case of Smallholder Dairy Farmers in Western Kenya Farmers ’ Perceptions of Dairy
Cattle Breeds , Breeding and Feeding Strategies : A Case of Smallholder Dairy Farmers in
Western Kenya. 8325. https://doi.org/10.1080/00128325.2019.1659215
Mader, T. L., & Davis, M. S. (2003). Environmental factors influencing heat stress in feedlot
cattle 1 , 2. 712–719.
Malafosse, A. (1990). Propagation of improved breeds : the role of artificial insemination and
embryo transfer. 9(3), 811–824.
26
Management, R., Development, R., Indigenous, S., Improvement, C., Okeno, T. O., Kahi, A. K.,
… Group, G. (2012). Tropentag 2012. 1, 1–4.
Martínez, M. E., Ranilla, M. J., Tejido, M. L., Saro, C., & Carro, M. D. (2010). Comparison of
fermentation of diets of variable composition and microbial populations in the rumen of
sheep and Rusitec fermenters . II . Protozoa population and diversity of bacterial
communities 1. Journal of Dairy Science, 93(8), 3699–3712.
https://doi.org/10.3168/jds.2009-2934
Mbuthia, J. M., & Rewe, T. O. (2014). Evaluation of pig production practices , constraints and
opportunities for improvement in smallholder production systems in Kenya. (December).
https://doi.org/10.1007/s11250-014-0730-2
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of Total Genetic Value
Using Genome-Wide Dense Marker Maps.
Miller, S. (2010). Revista Brasileira de Zootecnia Genetic improvement of beef cattle through
opportunities in genomics. 2010, 247–255.
Montiel, D. O., Udo, H. M. J., Frankena, K., & Zijpp, A. Van Der. (2017). ‘ La fiebre de Malta
’ : An Interface of Farmers and Caprine Brucellosis Control Policies in the Baj ıo Region ,
Mexico. 64, 171–184. https://doi.org/10.1111/tbed.12359
Morton, J. E. V, Frentz, S., Morgan, T., Sutherland-smith, A. J., & Robertson, S. P. (2016).
Biallelic Mutations in CYP26B1 : A Differential Diagnosis for Pfeiffer and Antley – Bixler
Syndromes How to Cite this Article : (July), 2706–2710.
https://doi.org/10.1002/ajmg.a.37804
Muasya, T. K., Peters, K. J., Magothe, T. M., & Kahi, A. K. (2014). Random regression test-day
parameters for first lactation milk yield in selection and production environments in Kenya.
Livestock Science, 169, 27–34. https://doi.org/10.1016/j.livsci.2014.09.012
Muriuki, H., Omore, A., & Hooton, N. (2003). The Policy environment in the Kenya dairy subsector : A review. (December).
Musallam, I., Prisca, A., Yempabou, D., Ngong, C. C., Fotsac, M., Mouiche-mouliom, M., …
Guitian, J. (2019). Acta Tropica Brucellosis in dairy herds : A public health concern in the
milk supply chains of West and Central Africa. Acta Tropica, 197(May), 105042.
https://doi.org/10.1016/j.actatropica.2019.105042
Nakaya, A., & Isobe, S. N. (2012). Will genomic selection be a practical method for plant
breeding ? 1303–1316. https://doi.org/10.1093/aob/mcs109
Ndumu, D. B., Baumung, R., Wurzinger, M., Drucker, A. G., & Okeyo, A. M. (2008).
Performance and fitness traits versus phenotypic appearance in the African Ankole
Longhorn cattle : A novel approach to identify selection criteria for indigenous breeds. 113,
234–242. https://doi.org/10.1016/j.livsci.2007.04.004
Nielsen, R. (2005). Molecular Signatures of Natural Selection.
https://doi.org/10.1146/annurev.genet.39.073003.112420
Nishio, M., Kahi, A. K., & Hirooka, H. (2008). Accounting for numbers of expressions of
specific genotypes using a modified gene-flow method. 114, 241–250.
27
https://doi.org/10.1016/j.livsci.2007.05.009
OPTIMISING DAIRY CATTLE BREEDING SYSTEMS INCORPORATING. (2019).
Palsson-mcdermott, E. M. (2013). From cancer to inflammatory diseases. 965–973.
https://doi.org/10.1002/bies.201300084
Pedersen, L. D., Sørensen, A. C., Henryon, M., Ansari-mahyari, S., & Berg, P. (n.d.). Author ’ s
personal copy Short communication ADAM : A computer program to simulate selective
breeding schemes for animals. https://doi.org/10.1016/j.livsci.2008.06.028
POLICY AND DEVELOPMENT PRODUCTIVITY TRENDS AND PERFORMANCE OF
DAIRY FARMING IN KENYA Stella Wambugu , Lilian Kirimi and Joseph Opiyo. (2011).
Pomeroy, R., Bravo-ureta, B. E., Solís, D., & Johnston, R. J. (2008). Bioeconomic modelling and
salmon aquaculture : An overview of the literature Bioeconomic modelling and salmon
aquaculture : an overview of the literature. (September).
https://doi.org/10.1504/IJEP.2008.020574
Ponzoni, R. W., Newman, S., & Ponzoni, R. W. (2014). Production : Developing breeding
objectives for australian beef cattle DEVELOPING BREEDING OBJECTIVES FOR
AUSTRALIAN BEEF. (September 2010), 35–47.
https://doi.org/10.1017/S0003356100004232
Rademaker, C. J., Bebe, B. O., & Lee, J. Van Der. (n.d.). Sustainable growth of the Kenyan dairy
sector Sustainable growth of the Kenyan dairy sector A quick scan of robustness , reliability
and resilience.
Ragkos, A., & Abas, Z. (2015). Using the choice experiment method in the design of breeding
goals in dairy sheep. Animal, The International Journal of Animal Biosciences, 9(2), 208–
217. https://doi.org/10.1017/S1751731114002353
Rege, J. E. O., Marshall, K., Notenbaert, A., Ojango, J. M. K., & Okeyo, A. M. (2011). Pro-poor
animal improvement and breeding — What can science do ? ☆. 136, 15–28.
https://doi.org/10.1016/j.livsci.2010.09.003
Richards, S., Vanleeuwen, J., Shepelo, G., Gitau, G. K., & Kamunde, C. (2015). Associations of
farm management practices with annual milk sales on smallholder dairy farms in Kenya. 8,
88–96. https://doi.org/10.14202/vetworld.2015.88-96.
Roessler, R., Herold, P., Willam, A., Piepho, H., Thuy, L. T., & Zárate, A. V. (2009). Modelling
of a recording scheme for market-oriented smallholder pig producers in Northwest Vietnam.
Livestock Science, 123(2–3), 241–248. https://doi.org/10.1016/j.livsci.2008.11.022
Rutten, M. J. M., & Bijma, P. (2002). SelAction : Software to Predict Selection Response and
Rate of Inbreeding in Livestock Breeding Programs. 93(6), 456–458.
Sagwa, C. B., Okeno, T. O., & Kahi, A. K. (2019). Increasing reproductive rates of both sexes in
dairy cattle breeding optimizes response to selection. 49(4).
Sagwa, C. B., Okeno, T. O., & Kahi, A. K. (2020). Including protein yield and mastitis resistance
in dairy cattle breeding goal optimizes response to selection. 49(6).
Sivarajasingam, S. (1995). A method to estimate economic weights for traits of disease
resistance in sheep. Australian Association for the Advancement of Animal Breeding and
28
Genetics, 11, 65–69.
Sölkner, J., Grausgruber, H., Mwai, A., & Peter, O. (2008). Breeding objectives and the relative
importance of traits in plant and animal breeding : a comparative review. 273–282.
https://doi.org/10.1007/s10681-007-9507-2
Staal, S. J., Waithaka, M. M., Consultant, I., Njoroge, L., & Njubi, D. (2014). Costs of milk
production in Kenya Estimates from Kiambu ,. (March 2003).
https://doi.org/10.13140/2.1.2945.9206
Tinker, N. A., Bekele, W. A., & Hattori, J. (2016). Haplotag : Software for Haplotype-Based
Genotyping-by-Sequencing Analysis. 6(April), 857–863.
https://doi.org/10.1534/g3.115.024596
Udo, H. M. J., Aklilu, H. A., Phong, L. T., Bosma, R. H., Budisatria, I. G. S., Patil, B. R., …
Bebe, B. O. (2011). Impact of intensi fi cation of different types of livestock production in
smallholder crop-livestock systems ☆. Livestock Science, 139(1–2), 22–29.
https://doi.org/10.1016/j.livsci.2011.03.020
Van Grevenhof, E. M., Van Arendonk, J. A., & Bijma, P. (2012). Response to genomic
selection: The Bulmer effect and the potential of genomic selection when the number of
phenotypic records is limiting. Genetics Selection Evolution, 44(1), 272–298.
Wahinya, P. K. (2020). Estimation of genetic parameters for milk and fertility traits within and
between low , medium and high dairy production systems in Kenya to account for
genotype-by-environment interaction. (March). https://doi.org/10.1111/jbg.12473
Wahinya, P., Okeno, T. O., & Kahi, A. K. (2015). Journal of Animal Production Advances
Economic and Biological Values for Pasture-Based Dairy Cattle Production Systems and
their Application in Genetic Improvement in the Tropics. (January).
https://doi.org/10.5455/japa.20150517032130
Wambugu, P. W., Furtado, A., Waters, D. L. E., Nyamongo, D. O., & Henry, R. J. (2013).
Conservation and utilization of African Oryza genetic resources. 1–13.
Wolc, A., Stricker, C., Arango, J., Settar, P., Fulton, J. E., Sullivan, N. P. O., … Dekkers, J. C.
M. (2011). Breeding value prediction for production traits in layer chickens using pedigree
or genomic relationships in a reduced animal model. 1–9.
Wolc, A., Zhao, H. H., Arango, J., Settar, P., Fulton, J. E., Sullivan, N. P. O., … Dekkers, J. C.
M. (2015). Response and inbreeding from a genomic selection experiment in layer
chickens. Genetics Selection Evolution, 1–12. https://doi.org/10.1186/s12711-015-0133-5
Yan, W. (2019). OPEN LG biplot : a graphical method for mega-environment investigation using
existing crop variety trial data. Scientific Reports, 1–8. https://doi.org/10.1038/s41598-01943683-9
29
30