Vulnerability and adaptation strategies to climate
variability of the Bos-taurusdairy genotypes in
Nandi South and Rongai Counties in Kenya
WORKING PAPER SERIES No. 73

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

1

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

2Vulnerability and adaptation strategies toclimate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai
Counties in Kenya

Kiplangat Ngeno
Animal Breeding and Genetics Group,
Department of Animal Sciences,
Egerton University, Egerton, Kenya.
Bockline O. Bebe
Livestock Production Systems Group,
Department of Animal Science, Egerton
University, Egerton, Kenya.

	
  ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

3This paper should be cited as:
African Technology Policy Studies Network, ATPS 2013:Vulnerability and adaptation
strategies to climate variability of the Bos-taurusdairy genotypes in Nandi South and Rongai
Counties in Kenya [Kiplangat Ngeno, Bockline O. Bebe], ATPS WORKING PAPER No. 73© 2013Published by the African Technology Policy Studies Network

ISBN: 978-9966-030-60-3

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

4

Table of Contents
Acknowledgement							

6Abstract 									

71. Introduction								

82. Literature Review							

103. Methodology								

124. Results and Discussion							

145. Conclusion and Recommendations					

22References								

24ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

5

Acknowledgement
This report was produced as part of the implementation of the African Technology Policy
Studies Network (ATPS) Phase VI Strategic Plan, 2008 – 2012 funded by ATPS Donors
including theMinisterie van Buitenlandse Zaken (DGIS) the Netherlands and the Rockefeller
Foundation. The authors hereby thank the ATPS for the financial and technical support during
the implementation of the program. The Authors specially thank Prof. Kevin Chika Urama,
the ATPS Executive Director for his visionary leadership as Program Director; Prof. Atieno
Ndede-Amadi, Program Coordinator; Dr. Nicholas Ozor, Senior Research Officer, ATPS;
Mr. Ernest Nti Acheampong, ATPS Research Officer and the ATPS Team for the technicalsupport during the research process.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

6Abstract
The study assessed the vulnerability and adaptation strategies of the Bos-taurus dairy breed/
genotypes (DBG) to climate variability of in two diverse production environments (PEDs)
namely Rongai and Nandi South counties. Long-term climatic data analysis presents a
declining rainfall trend and ascending trend in temperature in both production environments.
Through farmers’ perception ranking, the impacts of climate change and variability (CCV) on
dairy breed/genotype ranged from low (2.60) to high (3.96) in the production environments.
Impacts were high for seasonal heat stress (3.74), droughts (3.96), flooding (3.57) and feed
resource shortage (3.80) and moderate for disease epidemic (3.39) and water resource
decline (3.26) in Nandi South County. On the other hand, impacts in Rongai County werehigh for frequent droughts (3.60), disease epidemic (3.87) and feed shortage (3.60) andmoderate for heat stress (3.30), flooding (2.60) and water resource decline (3.10). The
current and earlier production systems in the two PEDs as well as response strategies to
impacts of CCV were different in the two counties. The results from the study indicate that the
DBGs are vulnerable to impacts of CCV. Production systems in different PEDs have evolved
in response to effects of climate variability. Different response strategies have been adopted
and used by farmers depending on the PEDs to mitigate the Climate change and variability.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

71. Introduction
1.1 Background information
Climate change and variability (CCV) in Sub-Saharan Africa is already impacting negatively
on rain-fed agriculture and livestock systems. The CCV are caused by both natural and humanrelated activities. There are many complex and interrelated issues that contribute to this stateof affairs. Probably more than 90% of the activities of humankind are largely responsible
for the current day CCV. Climate change and variability complexities are exacerbated byincreasing human population and demand for more agricultural land for food production,
resulting in the destruction of the vegetation cover and subsequently rampant environmental
degradation (Gordon et al., 2010). The demand for food, fuel wood (charcoal and firewood)and other forest products (including timber and poles for building and construction) increase
this problem.
Kenya is already experiencing a number of hazards resulting from CCV including more
frequent droughts, prolonged dry spells, intense rainfall and flush floods, increased heatstress and disease outbreaks (IPCC, 2007). These climatic hazards are accompanied bymore changes in the productivity of rain-fed crops and forage, reduced water availability andmore widespread water shortages, changing severity and distribution of important human,livestock and crop diseases.
The Fourth Assessment Report (4AR) of Intergovernmental Panel on Climate Change (IPCC,
2007; Morton, 2007) demonstrates the relationship among heat stress, declines in physicalactivity and the associated direct and indirect declines in animal feed intake. High ambient
temperature and reduced feed intake put a limitation on milk yield and this can reach a third
to half of the potential yield of a dairy cow. In the United state of America, maintenanceenergy requirement of dairy cow can increase by 22% at 32oC compared to maintenancerequirement at 16oC which is much lower (Chase, 2009). When temperature increases from
16 to 32oC, dry matter intake decreases by 18% and subsequently milk yield decreases by32%.
Dairy farming is a key component of livestock industry in Kenya and is dominated by Bostaurus dairy breeds/genotypes (DBG) comprising Friesian, Ayrshire, Guernsey, Jersey
and crosses among themselves (Muriuki et al., 2004). The industry is a major source of
livelihood to a large majority of Kenyans and it contributes approximately 4% of Kenya’s
Gross Domestic Product (GDP) (though recent studies indicate 8%), acts as a source ofincome and employment to over 1.5 million smallholder dairy farmers in addition to 500,000
direct jobs in milk transportation, processing and distribution, and a further 750,000 in related
support services (Kenya Dairy Board, 2012).
The DBGs are raised in different agro-ecological zones under diverse production environments(PEDs) and are classified as vulnerable to poor feeding, heat load and disease incidences,which are projected to increase in frequency, intensity and magnitude with the increasingclimate variability and change. The PEDs thus has both direct and indirect climate effects on
DBG, which will adversely impact on their performance and therefore limit their potential for
providing food, nutrition, income and job securities to the Kenyans (Muriuki et al., 2004). The
direct effects on dairy include impacts on animal health, welfare, growth and reproduction,
while the indirect effects are due to the impact of climate change on the productivity of pastures
ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

8and forage crops (DEFRA, 2009). Ambient temperature has the greatest direct effect while
feed intake has the greatest indirect effect on dairy cattle performance (Lingington, 1990;
McManus et al., 2005). These climatic hazards are accompanied with more changes inquantity and quality of feeds.
The quality and quantity of the forage component of dairy cattle diet is likely to be affected by
impact of CCV due to changes in forage growth and dry matter (DM) yield. If mean rainfall
declines this would lead to soil moisture deficits which reduces DM yield and affect also thestage of maturity for forage. The stage of maturity at which the crop is cut is a major determinantof quality and in any altered climatic scenario the interplay between increasing quantity anddeclining quality would continue to be of major importance although the alterations in climate
may be favourable to conservation and reduce losses during either ensilage or hay-making(Rowlinson, 2008). There is also a possibility that climate change will lead to a shift in theforage species grown and indirectly affects DBG performance.
As temperature and carbon dioxide (CO2) levels change due to climate change, optimal
growth rates for different species also change; species alter their competition dynamics,
and the composition of mixed grasslands changes (Thornton et al., 2007). Rowlinson (2008)
reported that, an elevation of temperatures may lead to an increase in the hectarage ofmaize grown for silage and of alfalfa for hay. Rising temperatures increase lignification ofplant tissues, reducing the digestibility and the rates of degradation of plant species (IFAD,
2009). This condition may consequently leading to reduced nutrient availability for animals
and ultimately to a reduction in livestock production (Thornton et al., 2007).This does raise
concerns of securing livelihoods for the poor, creating the necessity to identify options
and strategies for climate variability (CCV) adaptation by poor dairy farmers. Intervention
measures are needed to contribute towards building adaptive capacity and resilience toclimate variability in the short-term and climate change in the long-term.
1.2 Rationale
Comprehensive information on the PEDs of the DBG will; (a) facilitate better understandingof the potential impacts and consequences of CCV, (b) increase industry capability to
manage the implications of CCV, (c) inform and engage industry stakeholders to improveunderstanding of CCV issues, (d) facilitate better understanding of the likely impacts andopportunities associated with reducing and/or offsetting CCV for the dairy industry and (e)enhance understanding needed on the likely impacts of CCV on the vulnerability of the DBG,(f) facilitate understanding of the resilience to current CCV as well as to the risks associatedwith longer-term CCV to be gauged, and appropriate actions set in place to increase or
restore resilience where this is threatened. All these action combined contribute to securing
livelihood assets for the poor.
The objective of the study was to assess the vulnerability and adaptation strategies of DBGs
to CCV under PEDs inguiding the identification of appropriate breeding strategies that
can help dairy farmers mitigate CCV and improve productivity and adaptation for secured
livelihoods to the poor. Both natural and human induced CCV are considered in this study.
Specific objectives of the study include:• To characterize the diverse production environments (PED) for dairy production bydegree of vulnerability to CCV.
• To assess impact of climate variability and change based on farmer’s perception in the
different production environments.
• To rank vulnerability and general performance of the dairy breeds/genotypes (DBG) to
CCV in the identified PEDs based on farmer’s perception and experience.
• To examine diversity and dynamics of dairy production systems in the identified PEDs
and adaptation strategies in response to CCV.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

92. Literature Review
Kenya has 17,467,774million dairy cattle population (KNBS, 2009) of which 3,355,407 and14,112,367 are exotic and indigenous respectively. Eighty percent of these cattle are ownedby smallholder farmers (Bebe et al., 2002). Most of the smallholder farmers are found in thehighlands due to the favourable agro-ecology in these areas for dairy and crop production.
The main dairy production systems in the country are zero-, semi-zero- and free-grazing
systems. Zero-grazing dominates in the highlands where landholdings are declining andit is mostly a way of intensifying dairying (Bebe et al., 2003b). The most preferred DBG
is the Friesian followed by the Ayrshire. Other dairy breeds like the Jersey and Guernsey,
and the dual-purpose breeds like the Sahiwal, Brown Swiss, Red Poll, Small East African
Zebu, Zebu and Boran (for the pastoralists) are also reared. Bebe et al. (2003a) quantified
the breed preference for high milk production as 78%, 59%, 47% and 22% for Friesian,Ayrshire, Guernsey, and Jersey and the indigenous breeds, respectively. The exotic DBGs
were introduced in 1902 by the European settlers (Kenya Dairy Board, 2012).
Smallholder dairy farmers produce milk in diverse production environments where productivityof dairy breeds is increasingly challenged directly and indirectly with the impacts of variable
and changing climate. Increasing ambient temperatures directly impact heat load stress on
the animals while feed resource base worsens with more frequent droughts and climatechange induced outbreaks of transboundary animal diseases (Bebe et al., 2010). These
stresses are expected to increase in magnitude with the projected changing and variableclimate, which will further depress dairy productivity levels.
In their choice of dairy breeds, smallholder dairy farmers hardly pay attention to breed
tolerance and adaptability to the climate related stresses (Bebe et al., 2003a; King et al.,2006). They utilize Friesian, Ayrshire, Guernsey or Jersey breeds in production environments
already classified as hotspots of CCV, which include highlands, Semi-arid rangelands,
Coastal and Great-lakes ecosystems where Thornton et al. (2006) projects an increased
severity of climate variability and change. Dairy cattle performance is best at temperatures
between 4 to 240C, but temperatures frequently rise above this thermal comfort zone in thesmallholder dairy production environments (King et al., 2006; Thornton et al., 2009) due toCCV. Under high temperature and high rates of solar radiation, dairy cows decrease theirfeed intake to reduce their digestive heat production and they also avoid grazing during the
hot mid-day hours, consequently resulting to lesser grazing time which translates to overall
poor productivity. However, smallholder dairy farmers may face a feed resources constraint
which worsens with frequent droughts and flooding associated with the changing climate.
This change is associated with reduction in herbage growth rate, quality, species composition
and DM yield. This leads to reduced nutrient available to the animals and ultimately leads
to a reduction in their productivity (Thornton et al., 2007). Heat and nutritional stresses indairy cows reduce herd productivity and profitability through mortality, reduced growth andreproduction, which can be of substantial economic loss to producers utilising less adaptablebreeds. Heat stress has a variety of detrimental effects on livestock with significant effects
on milk production and reproduction in dairy cows (Fuquay, 1981; Johnson, 1987; Valtorta,
1996b). In Kenya, heat and nutritional effects on DBGs under PEDs have not yet beenstudied. In addition, the DBGs bred with imported semen could be less adaptable to more
frequent outbreaks of CCV induced diseases, especially the Rift Valley Fever, Foot and
ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

10Mouth Disease and East Coast Fever, further impacting adversely on productivity of the
DBGs. The outbreaks can cause increased production losses as well as lowering productquality and raising safety issues and consequently translating into reduced opportunities forthe dairy products in the domestic and export markets.
Attaining sustainable dairy productivity requires utilizing breeds adaptable to the variable
and changing climate in order not to enter state of insecurities in food, nutrition, income andhealth of smallholder dairy farmers and their livestock assets. When adaptable dairy breeds
are identified, they can be promoted in appropriate environments together with appropriate
management interventions that effectively reduce animal stresses to changing climate toenable smallholder dairy farmers mitigate and adapt to effects of changing climate to maintainand sustain their livelihood assets.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

113. Methodology
3.1 The study area
The criterion for the selection was guided by the need to have many trainees and develop
pThe study was carried out in Rongai county which lies in the latitude and longitude of 0°
10’ 0” S and 35° 51’ 0” and Nandi South County within latitudes 0° and 0°34″ North andlongitudes 34° 44″ and 35° 25″ East administrative counties of Kenya.The PED’s wereselected from lowland (Rongai County) and highland (Nandi South County) which represent
varying degree of climatic conditions in Kenya. Nandi south Countyaltitude ranges from1,400m-2,400m above sea level, with average temperatures of 18-25°C and annual rainfall
between 1,200mm-2000mm. On the other hand, Rongai County receives annual precipitationof 927mm and average temperatures ranging from 25-37°C.
3.2 Data collection
In each county, one location and all sub-locations per location were chosen in consultation
with officers from the ministry of livestock and development, ministry of agriculture, local
community based organization, and the local provincial administration (chiefs and theirassistants) for the survey. One location per county prominent in dairy production wereselected on the basis that they have the highest populations of the DBGs. The sub-location
included Boito, Lengenet and Mogotio sub-locations in Rongai and Mosombor, Toretmoi, andKimolwo sub-locations in Nandi South County. Two pairs of major landmarks were selectedin each sub-locations and transect lines were drawn between each pair. Simple random
sampling procedure was then used to select households for interviews by randomly picking
the 5th households.
The following measures were undertaken to test for reliability and validity of the research
instruments:••

Assessment of the presentation and relevance of the questionnaire to ensure that thequestions were relevant, reasonable, unambiguous and clear.
To ensure that the content and format of the instrument was consistent with the objectivesof the research and the sample of subjects measured, pretesting of the questionnairewith ten (10) people from the target population was done in Nakuru County.

•
3.3. Measurements of Variables
Quantitative research approach was employed in this study. Quantitative data were obtainedby interviews. A total of 100 smallholder farmers per location who rely on rain-fed agriculture
under natural production environment were interviewed. A pretested structured questionnaire
was used to gather information. Interviews were done in all the households. During previsits, local enumerators were recruited in each location and trained by the researchers.
Local enumerators were employed for ease of acceptability and communication within thecommunities.
The questionnaire was used to explore the farmers’ available knowledge on general
information, cattle breeds, cattle performance, production systems, vulnerability of the DBG
based on farming experience, constraints to production and response strategies. Researchmethods embraced a participatory approach, following the guidelines presented by FAO/
WAAP (2008). Scoring, ranking, seasonal calendar analysis, seasonal trend analysis
techniques were used. The questionnaire captured climate change effects of heat load, feed
ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

12resource shortage, water shortage, disease incidence, vector incidence and flooding levels
under normal weather, before change in normal weather and after change in normal weather
in three different degree of climate induced stress (high, medium and low) throughout thewet, normal and dry seasons.
The data collected was analyzed descriptively using frequencies, percentages, means andcross tabulation using the SPSS (Version 17) computer programme.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

13

4. Results and Discussion
4.1 Characterization of the diverse production environments
Figure 1 and 2 show rainfall trends in the two PEDs of Nandi South County and Rongai
County respectively. Climatic data analysis from the study reveals a declining rainfall trend
and ascending trend in temperature, which agrees with reports from literatures that CCV
are taking place (IPCC, 2007). Total annual precipitation range from 821.1mm-1647.70mm,
with average minimum temperatures of 8.340C and maximum of 24.010C (Figure 3). Annual
rainfall between 407.01mm-1407.52 mm (Figure 2) were recorded in Rongai County with theaverage temperature ranging from 16.660C- 36.400C (Figure 4).

Figure 1: Rainfall trend in Nandi South County

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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Figure 2 Rainfall trend in Rongai County

Figure 3: Maximum and minimum temperature for Nandi South County

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

15

Figure 4 Maximum and minimum temperature for Rongai County
In Kenya, temperatures have risen by 1°C over the past 50 years (GoK, 2009) and warming
is expected to accelerate with temperatures rising by nearly 3°C by 2050 (IPCC,2007). This
increase has been higher from March to May. According to Kenya meteorological department
report (2008), the prolonged and severe droughts in Kenya are widely perceived to be
symptomatic of the changing climate.
4.2 Perception of climate variability and change impact
Table 1 shows the farmers perception on CCV impacts in the different production environment.
A scale of 1 to 4 (1 = No, 2 = mild, 3 = moderate,4 = high) were used to assess the impactof seasonal heat, drought, flooding, disease epidemic, feed resource shortage and water
decline stress induced by CCV. The impacts were low (2.60) to high (3.96) (Table 1). Impacts
were high for seasonal heat stress (3.74), droughts (3.96), flooding (3.57) and feed resource
shortage (3.80) and moderate for disease epidemic (3.39) and water resource decline (3.26)
in Nandi South County. On the other hand, impacts in Rongai County were high for frequent
droughts (3.60), disease epidemic (3.87) and feed shortage (3.60), moderate for heat stress(3.30) and water resource decline (3.10) and low for flooding (2.60). The impacts affect DBGs
both directly and indirectly. Heat stress affects animals directly whereas drought, flooding,disease epidemic, feed resource shortage and water decline affects indirectly. Hot dry
conditions during the grazing period can reduce the quality and consistency of the feed, which
may lead to animal welfare and productivity problems (DEFRA, 2009). Furthermore, ambient
temperatures above 5–25oC thermal comfort zone are often experienced in smallholder
production environments in Kenya and depress productivity (King et al., 2006). This is throughdepressed feed intake and reduced grazing time in order to reduce digestive heat produced(McManus et al, 2009). From farmers’ points of view, drought is the main cause for low feed
quantity and the little available are poor quality. According to Thornton et al. (2006), CCV is
associated with changes in herbage growth, quality and dry matter yield which is agreementwith the findings of this study. Changes in rainfall and humidity are often considered as the
key necessary parameters which modulate the emergence of various animal diseases andthis is confirmed by moderate to high impacts in the two PEDs. Baker et al. (1998) reported
that, changes in rainfall and temperature regimes influence the abundance and distributionof disease vectors, causing an increased incidence of disease, often contributing to reduction
ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

16

in animal productivity and possibly increase animal mortality. The moderate (Nandi south
County) to high (Rongai) impact of diseases may be attributed to widespread diseases and/or “exotic” diseases becoming more apparent in the PEDs. The low (2.60) to high (3.96)
impacts of climate change in these regions are negatively influencing on the dairy production
systems which are consistent with the IPCC report (2007) which suggested that CCV impacts
are possible factors which contributed to a shift in the dairy production system in the PEDs.
Table 1: Perception of climate change impacts in the production environments
CountyClimate change impacts
Seasonal
Droughts
heat stressflooding

DiseaseepidemicFeed
resource
shortage

Water
resource
decline

Nandi
South

3.74

3.96

3.57

3.39

3.80

3.26

Rongai

3.30

3.60

2.60

3.87

3.60

3.10

4.3 Impacts of climate vulnerability and change (CCV) in general performance(production and reproduction) of various DBGs
A scale of 1 to 5 (1=low, 2 = fair, 3 = good, 4 = very good, 5 = excellent) was used to assess
the impacts of CCV on general performance of various DBGs based on farmer’s perception.
General performance included production (milk and growth rate) and reproduction (fertility ingeneral for cows and bulls, calving rate, calving interval and conception rate). In Nandi SouthCounty during the period of ‘medium heat load’ in the past (20 years ago), Friesian (3.52),
Ayrshire (3.55), Guernsey (3.00), Jersey (3.00), dairy crosses (3.27) and dairy-zebu crosses(3.00) had good to very good performance (Table 2). For the same region under‘medium
heat load after’ (20 years from now and beyond), DBGs are expected to perform differently
between fair to good (Friesian (2.40), Ayrshire (3.17), Guernsey (3.00), Jersey (3.00), dairy
crosses (3.02) and dairy-zebu crosses (2.50) as shown in Table 2. In the past (20 yearsago) during ‘medium heat load, general performance in Rongai County were fair to good;
Friesian (2.08), Ayrshire (2.25), Guernsey (2.50), Jersey (2.25), dairy crosses (1.82), dairyzebu crosses (2.40) and zebu (2.60) (Table 2). The DBG in the same Rongai region 20 years
from now and beyond (‘medium heat after’) under medium heat load are expected to perform
between low to fair is expected; Friesian (1.85), Ayrshire (1.75), Guernsey (2.50), Jersey(1.75), dairy crosses (1.73), dairy-zebu crosses (1.78) and zebu (2.25) as shown in Table 2.
The findings from the study revealed that CCV may impact both direct and indirectly on DBG
general performance. According to DEFRA, (2009), the direct effects may include impacts
on animal health, welfare, growth and reproduction, and indirectly on the productivity of
pastures and forage crops which are in agreement with the findings of this study. In the two
Counties, high heat load is expected to reduce DBGs performance from good to poor (Table2).The study proofed the detrimental effects of heat stress on animals. Johnson (1987) andValtorta (1996b) reported that heat stress have significant effects on milk production andreproduction in dairy cows. Dairy cattle show signs of heat stress when the temperaturehumidity index (THI) is higher than 72 (Armstrong, 1994). The comfort limit depends on thelevel of production. Animals with higher level of production are more sensitive to heat stress(Johnson, 1987) which is consistent with the findings of this study. Impacts of heat stress werehigher in Rongai. Rongai is hot (36.40C) and dry (annual precipitation as low as 407mm).
Hot dry conditions during the grazing period can reduce the quality and consistency of thefeed, which may lead to productivity problems (DEFRA, 2009). Rowlinson (2008) revealed
that extreme high temperatures may subject the dairy cow with thermal stress and this may
result to reduced feed intake and performance.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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Table 2 The DBG general performance levels under normal weather, before change in normal
weather, and after change in normal weather in Nandi South and Rongai counties
CCV effects

Heat
load

Degree ofstressFriesianAyrshire
Guernsey
JerseyDairy crosses
Dairy Zebu
crosses
Zebu

Before

After

high

medium

low

Nandi

2.52

3.52

Rongai

2.23

2.08

Nandi

2.47

Rongaihighmediumlow

4.64 1.86

2.40

4.00

2.50 1.69

1.85

2.50

3.55

4.90 2.41

3.17

4.34

2.13

2.25

3.00 1.75

1.75

2.29

Nandi

2.00

3.00

4.00 2.00

3.00

4.00

Rongai

2.25

2.50

3.00 2.25

2.50

2.75

Nandi

1.40

3.00

4.00 1.60

3.00

4.00

Rongai2.25

2.25

2.75 2.00

1.75

2.50

Nandi

2.46

3.27

4.98 2.02

3.02

4.06

Rongai1.50

1.82

2.60 1.50

1.73

2.10

Nandi

3.00

3.00

4.00 1.50

2.50

4.00

Rongai

2.30

2.40

2.70 1.56

1.78

2.29

Nandi

-

-

-

-

-

Rongai

2.00

2.60

3.25 2.00

2.25

2.50

-

4.4 The diversity of dairy production system utilised in PEDs
Several livestock production systems exist and farmers practice the most suitable dependingon the magnitude of the resource, production environment, major outputs and a setof productivity indices. To identify the dairy production systems and if they are changing
with time, farmers were asked the type of the system they were using currently and what
they have been using in the past (20 years ago). Nandi South County in the past and thepercentage of farmers who adopted were; crop-livestock (15.6%), agro-forestry-livestock(35.4%) and scavenger (49%) systems. Due to the effects of CCV, dairy production systems
have changed. The current production systems with the percentages of farmers practicing
were; pastoralism (9%), crop-livestock (68%), agro-forestry-livestock (1%) and scavenging(5.2%) systems whereas 16.8% stopped dairy farming in Nandi South County (Figure 5). In
Rongai County, 6%, 26%, 39%,16% and 13% of farmers practiced ranching, pastoralism,
crop-livestock, agro-pastoralist and scavenger systems respectively earlier (Figure 6). Thesesystems in Rongai have shifted to 6 %, 24.9%, 36.8%, 11% and 21.3% of farmers practicing
ranching, pastoralism, crop-livestock, agro-pastoralist and scavenging systems.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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Figure 5: Earlier and current dairy production system in Nandi South County

Figure 6: Earlier and current dairy production system in Rongai County

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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4.5 Vulnerability of the various DBG to climate variability and change
Current relative vulnerability ranking of DBG to CCV impacts in the PEDs was done in afour scale ranking order; 1= not vulnerable; 2= mild vulnerable; 3= moderately vulnerable
and 4= highly vulnerable. Friesian was the most vulnerable genotype under all levels ofheat stress as compared to other genotypes in Nandi South County (Table 3). Vulnerabilities
were moderate to high for Friesian (3.08-3.92), Ayrshire (3.13-3.75), Guernsey (2.75-3.50),
Jersey (2.75-3.50), dairy crosses (2.82-3.83), and dairy-zebu crosses (2.67-2.90) and mild
for zebu genotype (1.75-2.25) in Rongai county. In the face of climate challenges, adaptationof different dairy genotypes to tropical conditions might become highly imperative. Climate
determines the type of livestock most adapted to different agro-ecological zones and therefore
the genotypes that are able to sustain rural communities. For instance, under low heat stress,
in Nandi South County, Friesians were not vulnerable (1.00) and can do better than the same
Friesian under low heat stress (3.08) in Rongai (Table3). Vulnerability of Friesians to heatstress more than other DBGs could be attributed to lack of genes relevant to tropical heatstress. Certain major genes have been found to be relevant in tropical production environment
characterised by high heat load (Horst, 1989). Friesian high susceptibility than other DBGs
is because of its poor heat regulatory capacity, due to differences in metabolic rate, feed andwater consumption, sweating rate, and coat characteristics and colour. Friesian breed have ahigher heat loading at the skin, which have to evaporate substantially more sweat than other
DBGs to maintain normal body temperatures. In addition, exotic breeds like Friesian undertropical conditions, might show impaired productivity since genotype x environment (G x E)
interactions could lead to a reduction in the biological and economic efficiency. Therefore,
Rongai region which is currently characterised by high temperatures and low rainfall might shift
towards more adaptive genotypes such as dairy-zebu crosses and zebu. Indigenous cattle
breeds such as Zebu and its crosses are highly adapted to the harsh conditions, poor nutritionand disease and parasite challenges. Their adaptation is attributed to different hereditarycharacteristics that have resulted in differences in reactions to environmental stimuli. These
reactions are intimately associated with anatomical-physiological characteristics, which havedeveloped as a result of natural selection. Conversely, indigenous cattle breeds possess
genes and alleles that are pertinent to their adaptation to the local production environments.
Table 3: Vulnerability ranking of DBG to CCV impacts now in the production
environment
DBG

CountyHeat loadHigh

FriesianAyrshire
Guernsey
JerseyDairy Crosses
Dairy Zebu
Crosses

Medium

Low

Nandi

3.72

2.79

1

Rongai

3.92

3.38

3.08

Nandi

2.07

1.15

1

Rongai

3.75

3.5

3.13

Nandi

2

1

1

Rongai

3.5

3.25

2.75

Nandi

2

1

1

Rongai

3.75

3.25

2.5

Nandi

2.08

1.13

1.02

Rongai

3.83

3.33

2.82

Nandi

-

-

-

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

20

Zebu

Rongai

2.9

2.67

2.67

Nandi

-

-

-

Rongai

2.25

2

1.75

4.6 Adaptation strategies
Table 4: Response strategies for high heat load before normal weather change andhigh heat load now for FriesianResponse strategies

High heat load before (20 years
ago)High heat load now

Nandi South

Rongai

Nandi South

RongaiMore housing

0.0%

11.1%

2.3%

20.0%

Provide shade

53.5%

44.4%

4.7%

10.0%

Adopt housed system

0.0%

22.3%

0.0%

30.0%

Monitoring and disease
surveillance

0.0%

11.1%

0.0%

10.0%

Improve building insulation

0.0%

11.1%

0.0%

30.0%

Multiple strategies

46.5%

0.0%

93.0%

0.0%

Table 4 shows the most common response strategies to heat stress. Farmers (indicated inpercentage) indicated that the most preferred methods in both counties were provision ofshade, more housing, use of shelter systems, monitoring and disease surveillance, improved
building insulation or combination of multiple strategies. Response strategies for ‘high heatload before’ (high level of heat stress 20 years ago) normal weather change and ‘high heatload now (high level of heat stress currently-2011)’ varied between the two counties. The
main strategies for ‘high heat load before’ in Nandi South County were provision of shade(53.5%), and multiple strategies (46.5%). Multiple strategies combines two or more strategies
and combined strategies in the PEDs included shade, housing, housed system, monitoringand surveillance and/or improve building insulation etc. In Rongai county, main strategies
used were; more housing (11.1%), provision of shade (44.4%), adoption of housed system(22.3%), monitoring and disease surveillance (11.1%) and improved building insulation(11.1%) as shown in Table 4. Response strategies to heat stress 20 years ago and now
were different. During the period of ‘high heat load now’, 93.0% of farmers adopted multiple
adaptation strategies in Nandi South County. For Rongai County, more housing (20%),
housed system (30%) and improved building insulation (30%) were used. Zero-grazing
was not common in the two production environments. From the study, it was revealed that
response strategies may vary with production environments (as is the case with Rongai
and Nandi South County). Variation in response strategies could be because of diversified
characteristics of the two PEDs. Rongai district which lies in the latitude and longitude of 0°
10’ 0” S and 35° 51’ 0” is a lowland area and receives annual precipitation of 927mm andaverage temperatures ranging from 25-37°C. On the other hand, Nandi South district withlatitudes 0° and 0°34 North and longitudes 34° 44″ and 35° 25″ East is an highland region
with altitude ranging from 1,400m-2,400m above sea level, average temperatures of 18-25°C
and annual rainfall between 1,200mm-2000mm.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

215. Conclusions and Recommendations
The paper presents the vulnerabilities and adaptation strategies of Bos-taurus dairy
genotypes to CCV in Nandi South and Rongai Counties. The results from the study indicatethat the DBGs are vulnerable to impacts of CCV. Production systems in different PEDs
have evolved in response to effects of climatic variations. Different response strategies havebeen adopted and used by farmers depending on the PEDs to mitigate the impact of CCV.
Inferences generated from this study can provide a strong basis for mitigating CCV andimproving productivity through either genetic manipulation of their DBGs to the specific PED
or modification of production environment to suit the DBGs.
Genetic approach need to be developed and applied to help dairy industries mitigate impactsof CVC using genetic tools. Broader breeding goals have become the norm in the dairy,usually incorporating production and “fitness”(health, fertility, longevity) traits in developed
world. However, exotic cattle produced from such breeding programmes faces challenges
of genotype by environment interactions, hence there is a need to develop local breeding
programme for specific production environment. Therefore, an economic evaluation of thedifferent DBGs imported and across PEDs is necessary to identify the most suitable breed (s)for the different production environments. The assessment of the presence and magnitude of
DBGs by environment interaction is needed, which will enable the achievement of optimum
performance across the different environments. In the presence of breed by environment
interactions, particular genotypes will be sought for particular environments in order to
optimize total merit of production. Increased training to farmers on how to handle the DBGs
is also necessary.
Selection for breeds with effective thermoregulatory control may be exploited. Some
opportunities exist to improve DBG adaptation through manipulation of genetic mechanisms
at cellular level. Inclusion of traits associated with body condition, survival, rehydration, coatcolour and foraging ability traits which are considered most relevant to animal adaptability
to CVC stress as well as temperature, disease and parasite tolerance in breeding indicesfor species/breeds under threat e.g. genetics of CVC stress and G*E interactions areof paramount. For example, selection for heat tolerance based on rectal temperature
measurements and inclusion of a temperature-humidity-index in the genetic evaluation
models can be a promising option.
Efficient and affordable adaptation strategies which have been developed for farmers in the
PEDs were expensive adaptation technologies. Other practices to counter CVC impacts
which need to be promoted include; (a) sprinkling and ventilation to reduce heat stressfrom increased temperature, (b) modification of the grazing times. Mostly, farmers in CVC
hotspots need to maximise on night grazing. Cattle tend to graze two thirds of their intake
during the day and a third at night, but in hot weather this need to be changed to half during
the day and half night (Stewart, 2005), (c) changing herd composition such as shifting fromFriesian to Zebu in Rongai district, etc., (d) improvement of water resources management
infrastructure to harvest and store rainwater, such as small superficial and undergrounddams, tanks connected to the roofs of houses, etc. (Kurukulasuriya and Rosenthal, 2003;
FAO, 2008; Sidahmed, 2008; Thornton et al.,2008), (e) matching stocking rates with pastureproduction, altering the rotation of pastures, altering forage and animal species/breeds,
altering the integration within mixed livestock and crop systems including the use of adapted
ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

22forage crops (Howden, et al., 2007; World Bank, 2008), and (f) early milking so that the DBG
can get out to graze while the day is still cool and milk early in the afternoon so that the cows
can cool down when the day is still very hot.

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

23

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ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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3. Methodology
Study Area
The study area of this research is situated in the upper Blue Nile catchment of Ethiopia.
It is located approximately between coordinate 10033’06’’ to 10050’24’’ North latitude and37042’36’’ to 37058’24’’ East longitude. It covers a total estimated area of 58122.94 hectares
and with the total population of about 132069. Topographically, the area lies in the altitudes
range of 878m to 4000m.a.s.l (Figure 1).
Figure 1: Location of the study area (Source: Choke Mountain Initiative project
document, 2010)

As a result of this the area is characterized by three distinct agro-ecological zones- Dega,
Weynadega, and Kola. According to the traditional classification system, which mainly relies
on altitude and temperature for classification, Ethiopia has five climatic zones (Table 1).
The information which is obtained in Weredas’ Agricultural Offices revealed that the rainfall
amount and temperature of the area ranges from 385 -1300mm, and 10- 26oC respectively.
The area gets monomodal type of rain fall (that is Kirmt rain fall regime). The soil types wereidentified based on their colors (red, brown, black and grey); on the average percentage,
about 60 %, 39 %, 36% and 2.5% are brown, red, black and grey soil respectively. The
cultivated land covers a total area of 34161.93 ha of the area. The major annual crops
cultivated in the catchment are barley, Avena species (Ingedo), wheat, beans, peas, potato,maize, and sorghum. The common domestic animals in the area are cattle, sheep, goats,horses, mules, donkeys, and poultry.
Table 1: Traditional climatic zones and their physical characteristics
Zone

Altitude(meters)

Rainfall(mm/year)

Average temperature(Co)

Wurch(upper highland)

3200 plus

900- 2,200>11.5

Dega(highlands)

2,300- 3,200

900- 1,200

17.1/16.0- 11.5

ISBN: 978-9966-030-60-3

ATPS (2013): Vulnerability and adaptation strategies to climate variability of the Bos-taurusdairy
genotypes in Nandi South and Rongai Counties in Kenya. ATPS Working Paper No. 73

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