Livestock and environment spotlight KENYALivestock are a major user of water. Livestock production draws on water resources as drinking water, water to produce feed and water to clean

Financial support provided by the United States Agency for International Development (USAID)

KENYALivestock and environment spotlight

Cattle andpoultry sectors

Republic of Kenya

1

Livestock and Environment Spotlight

Cattle and Poultry in Kenya

Introduction

While the livestock sector provides a variety of goods and services to society, from food to income

to social functions, its complex interactions with the ecosystem and significant use of natural

resources has large social implications. The sector, is the world’s largest user of agricultural land,

considering both grazing and feed-crop lands, thereby having a major impact on soil, water and

air quality and biodiversity (FAO 2017a; Monfreda et al., 2008; Ramankutty et al., 2008).

Kenya’s growing population, rising incomes and urbanization are translating into an increased

demand for livestock products. One estimate, suggests that consumption of beef and milk will

increase by over 170 percent between 2010 and 2050; and that of chicken meat and eggs by 174

and 500 percent, respectively (ASL2050 FAO, 2017a). As a consequence, the livestock sector will

grow and transform, resulting in new and changed relationships between domestic animals, natural

resources and wildlife (FAO, 2009). In Kenya, over the past few years, land use changes associated

to livestock development have resulted in social conflicts, decline of wildlife and bird species, loss

in plant biodiversity, and reduced soil productivity (Maitima et al., 2009). Assessing the current

livestock impact on the environment is thus critical to understand how the coming growth and

transformation of the sector might impact society, and to identify actions to take today to ensure

a sustainable development trajectory of livestock in the long-term.

This brief summarises evidence of cattle and poultry impact on the environment in Kenya, building

on available research reports and papers. It explores the correlations between livestock and

greenhouse gases; livestock and water; livestock and grasslands; and livestock and biodiversity.

When data can be obtained, it provides specific evidence of cattle and poultry production systems’

impact on the environment. Livestock production systems were characterized by national

stakeholders - including the Ministry of Agriculture, Livestock and Fisheries, the Ministry of

Environment and Natural Resources and the Ministry of Health - as part of the One Health

approach implementation to facilitate assessment of the current and long-term impact of livestock

on the economy and people’s livelihoods, on public health and on the environment. They include

intensive, semi-intensive and extensive dairy production systems; pastoralism, agro-pastoralism,

ranching and feedlots for beef production; and free-range, semi-intensive and intensive poultry

meat production systems (Annex 1).

1. Livestock and Greenhouse Gas Emissions

Greenhouse gases from human activities are the most significant drivers of observed climate

change since the mid-20th century (IPCC, 2014). In 2010, agriculture, forestry and other land use

generated approximately one quarter of the total global greenhouse gas emissions (GHG)1 (IPCC,

2014). Kenya’s total GHG emissions are 60.2 Mt CO2eq. The country contributes to less than 0.1

1 This estimate does not include the CO2 that ecosystems remove from the atmosphere by sequestering carbon in

biomass, dead organic matter and soils, which offset approximately 20 percent of emissions from this sector.

2

percent of the total GHG emissions. Agricultural activities are the leading source of GHG

emissions, contributing to 62.8 percent (37.8 Mt CO2eq.) of the total emissions in 2013. The energy

sector comes second (18.7 Mt CO2eq. or 31.2%), followed by industrial processes and waste

management activities (3.7 Mt CO2eq or 6%) (WRI CAIT 2.0, 2017; GOK, 2015a).

Figure 1. Kenya’s GHG emissions by sector.

Source: WRI CAIT 2.0 (2017); FAOSTAT (2017)

In Kenya, livestock-related activities are estimated to contribute to 92 percent of the total GHG

emissions from agriculture, mainly via enteric fermentation (20.8 Mt CO2eq or 55 percent) and

manure left on pasture (13.6 Mt CO2eq or 36.9 percent) (WRI CAIT 2.0, 2017).

To quantify GHG emissions from the different cattle and poultry systems we use data from the

Global Livestock Environmental Assessment Model (GLEAM). GLEAM is a Geographic

Information System (GIS) framework that simulates bio-physical processes and activities along

livestock supply chains applying a life cycle assessment approach. GLEAM quantifies production

and use of natural resources in the livestock sector and measures GHG emissions from the sector,

to assess the effectiveness of alternative adaptation and mitigation options that support a

sustainable livestock development trajectory. GLEAM identifies three main groups of emissions:

upstream emissions include emissions related to feed production, processing and transportation.

Animal production emissions comprise emissions from enteric fermentation, manure management

and on-farm energy use. Downstream emissions are caused by the processing and post-farm

transport of livestock commodities. Three gases are considered in GLEAM: carbon dioxide (CO2),

methane (CH4) and nitrous oxide (N2O). All emissions are converted into CO2 eq. using the latest

global warming potential from IPCC (2014) (298 for N2O and 34 for CH4). We run the model

using 2010 data for animal numbers and distribution, herd parameters, feed yields and rations, and

manure management systems.

Figure 2 shows the total CO2 eq. emissions for chicken, dairy and beef production systems, which

amounts to 28 Mt CO2 eq. The beef systems, comprising over 14 million heads of cattle, produce

most of the emission, about 20.5 Mt of CO2 eq., contributing to over 73 percent of the total GHG

Agriculture EnergyIndustrialprocesses

Waste

MtCo2 e 37.8 18.7 2.8 0.9

0

5

10

15

20

25

30

35

40

Em

issi

on

s (

Mt

C02 e

q.)

3

emissions from the three systems. Dairy systems, comprising 4.5 million dairy cattle, generates 7.2

Mt CO2 eq. (26 percent) and poultry systems 0.26 Mt CO2 eq. (1 percent).

Figure 2. Tonnes of CO2 eq. emissions for dairy, beef and chicken production systems in Kenya

Source: FAO (2016a); authors’ calculations based on GLEAM 2.0 (FAO, 2017b) www.fao.org/gleam/en/

1.1. Poultry production systems and GHG emissions

Poultry production systems (chickens) emit a total of 262 744 tonnes CO2 eq. Intensive poultry

production systems (broiler farming) contribute to about 53 percent of all GHG emissions from

the sector, with the semi intensive and free-range production systems responsible for the remaining

47 percent. This difference seems negligible, but emissions per bird are five times higher in

intensive than in free range systems: one bird in intensive system emits 17 kg of CO2 eq. per year

versus 4 kg for a bird in extensive systems (Annex ii). GHG emissions in intensive systems mainly

originate from the processing and transporting used in feed production. In semi-intensive and

extensive systems, manure management contributes most to GHG emissions.

Chicken Dairy Beef

Total t Co2 eq. 262 744 7 209 844 20 508 317

0

5 000 000

10 000 000

15 000 000

20 000 000

25 000 000E

mis

sio

ns

of

CO

2eq

. (t

on

nes

)

chicken1%

dairy26%

Beef73%

% contribution of tCo2 eq emission

4

Figure 3. Tonnes of CO2 eq. emission by chicken (meat) production system


Figure 4. GHG emissions by source and poultry production system


1.2. Dairy production systems and GHG emissions

The dairy sector generates about 7.2 Mt CO2 eq. of GHG emissions, where over 78 percent are

due to enteric fermentation and about 21 percent to manure management. Small-scale intensive

and semi intensive systems have the highest emissions, contributing to 46.33 and 35.44 percent of

the total sector GHG emissions, respectively. Large scale intensive systems have lower GHG

0

50 000

100 000

150 000

200 000

250 000

300 000

Backyardimproved

indigenious

Free range IntensiveSmall Scale

IntensiveMedium

Scale

IntensiveLarge Scale

Total

emis

sio

ns

of

CO

2 e

q. (

ton

nes

)

0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

100 000

Semi-Intensive Free range Small Scale Medium Scale Large Scale

emis

sio

ns

of

CO

2eq

. (t

on

nes

)

fertilizer, applied manure, crop residues,

feed: rice feed, LUC: soy & palm,

manure management, CH4 manure management, N2O

5

emissions (9 percent) and extensive systems are even lower (4.35-4.85 percent). This is expected,

as about 80 percent of dairy animals are kept in small-scale intensive and semi-intensive production

systems (Bebe et al., 2016), where close livestock-crop interaction results in intensive use of animal

dung. On a per head basis, dairy cattle emissions in semi-intensive systems are the lowest (1.19

tCO2 eq. per year). Methane contributes to three quarters of the total GHG emissions from

manure management.

Figure 5. Tonnes of CO2 eq. emission by dairy production system


Figure 6. GHG emissions by source and dairy production system


0

500 000

1 000 000

1 500 000

2 000 000

2 500 000

3 000 000

3 500 000

4 000 000

Large ScaleIntensive

Small ScaleIntensive

Semi - intensive Extensive(controlled)

Extensive(uncontrolled)

emis

sio

ns

of

CO

2eq

. (t

on

nes

)

0

500 000

1 000 000

1 500 000

2 000 000

2 500 000

3 000 000

3 500 000

4 000 000

Large ScaleIntensive

Small ScaleIntensive

Semi - intensive Extensive(controlled)

Extensive(uncontrolled)

emis

sio

ns

of

CO

2eq

. (t

on

nes

)

Production system

enteric fermentation, CH4 manure management, CH4 manure management, N2O

feed emission, N2O feed emission, co2

6

1.3. Beef production systems and GHG emissions

The beef sector generates about 20.5 Mt CO2 eq. of GHG emissions, largely from animals in

pastoral and agro-pastoral areas. Enteric fermentation is responsible for about 77 percent of all

the sector’s emission. The sheer total number of animals in beef systems and production practices

largely explain these results. On a per head basis, cattle in feedlots emit more greenhouse gases per

year (about 2 tCO2 eq. per year), than animals in the other production systems (1.41, 1.46 and 1.41

tCO2 eq. per year in pastoralism, agro-pastoralism and ranching systems, respectively).

Figure 7. Tonnes of CO2 eq. emission by beef production system


Figure 8. GHG emissions by source and beef production system


0

2 000 000

4 000 000

6 000 000

8 000 000

10 000 000

12 000 000

ExtensivePastoralism

Semi - Intensive Extensive Ranching Feed Lot (Intensive)

Em

issi

on

s o

f C

O2

eq. (t

on

nes

)

0

2 000 000

4 000 000

6 000 000

8 000 000

10 000 000

12 000 000

ExtensivePastoralism

Semi - Intensive Extensive Ranching Feed Lot (Intensive)

Em

issi

on

s o

f C

O2

eq. (t

on

nes

)

enteric fermentation, manure management, CH4 manure management, N2O

feed emission, N2O feed emission, CO2

7

2. Livestock and water stress

Livestock are a major user of water. Livestock production draws on water resources as drinking

water, water to produce feed and water to clean and process. The amount of drinking water used

varies from 5–50 litres per Tropical Livestock Unit (TLU) per day and depends on the species, dry

matter intake, composition of the feed, water content of the feed, live weight of the animal, level

of milk and meat production, physiological status of the animal and the climate in which the

livestock is managed (Steinfeld et al., 2006; Lardy et al., 2008). The water required to produce daily

feed for livestock is about 100 times the daily drinking water requirements. Livestock typically

require daily feed intake of dry matter amounting to about 3 percent of their weight and about 500

l (0.5m3) of water is required to produce 1 kg dry matter (Peden et al., 2002). Thus, the impacts of

livestock production on local / regional water balances largely depend on the predominant feeding

systems, i.e. natural vegetation and / or crop residues vs. feed crops, and on feed production

practices, i.e. rainfed vs. irrigated.

Kenya is a water deficit country (World Bank, 2010) with more than 80 percent of its land being

arid and semi-arid (GoK, 2008). Total renewable water resources are estimated to be 30.7 billion

m3 per year, or 692 m3 on a per capita basis (World Bank, 2010). It is anticipated that by 2030

Kenya will fall under the absolute water scarcity threshold of 500 m3 per year (FAO, 2016b). About

64 percent of all water permits (5 057 million m³) are for agriculture, with livestock utilizing about

11 percent of the agricultural water (GoK, 2015c). In this brief, we rely on Mekonnen and Hoekstra

(2012) to assess the water footprint of farm animals by cattle and poultry production system and

by source of water, including blue, green and grey water. Blue water footprint refers to the amount

of water consumed from surface and groundwater along the value chain of a product, which is

evaporated after withdrawal. Green water refers to rainwater consumption and grey water

footprint refers to the volume of freshwater needed to assimilate the load of pollutants emitted.

The water footprint of a farm animal consists of direct consumption via drinking and service water

as well as indirect consumption through the water used for feed production (Chapaign and

Hoekstra, 2003 in Mekonnen and Hoekstra, 2012).

Figures 9 and 10 show the water footprint for cattle and poultry by production system respectively,

measured in cubic meters (m3) per tonne of live animal. The cattle water footprint is more than

10 000 m3 per tonne of live animal, on average. In grazing systems, it is 23 236 m3 per tonne and

in industrial production system about 2 000 m3 per tonne. In the three cattle production systems

considered here (grazing, mixed and industrial), green water footprint represents almost all of cattle

water consumption.

The average water footprint in poultry system is 3 693 m3 per tonne of live birds. Free range

systems have the highest water footprint (about 7 000 m3 per tonne) while industrial or intensive

systems have the lowest one (about 2 000 m3 per tonne). Not differently than for the cattle sector,

green water consumption explains almost all the water footprint in poultry production systems.

8

Figure 9. Green, blue and grey water footprint (m3 per tonne) by cattle production system

Source: authors’ calculations on data from Mekonnen and Hoekstra (2012)

Figure 10. Green, blue and grey water footprint (m3 per tonne) by poultry production system

Source: authors’ calculation on data from Mekonnen and Hoekstra (2012)

Figures 11 and 12 compare the cattle and poultry water footprint in Kenya with world averages.

The cattle water footprint in Kenya is higher than the world average. This is explained by the very

high water footprint of cattle in grazing systems: in arid and semi-arid areas of Kenya the water

footprint is more than double the world average. Conversely, the water footprint in mixed and

industrial systems is slightly lower in Kenya than in the world as a whole. As to poultry, Kenya’s

poultry water footprint is slightly higher than the world average. Note, however, that Kenya’s

poultry system largely, if not only rely on green water, whereas world average levels grey and blue

water footprints are much higher.

23 139

6 487

1 926

10 171

97

103

74

101

0

0

0

0

0

5 000

10 000

15 000

20 000

25 000

Grazing Mixed Industrial Weightedaverage

m3

per

to

nn

e o

f liv

e an

imal

Green Blue Grey

6 836

3 750

1 962

3 693

157

90

47

88

97

53

28

52

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

Grazing Mixed Industrial Weightedaverage

m3

per

to

nn

e o

f liv

e an

imal

Green Blue Grey

9

Figure 11. Green, blue and grey cattle footprint (m3 per tonne) by cattle production system in

Kenya and the world

Source: authors’ calculation on data from Mekonnen and Hoekestra (2012)

Figure 12. Green, blue and grey water footprint (m3 per tonne) by poultry production system in

Kenya and the world

Source: authors’ calculation on data from Mekonnen and Hoekestra (2012)

3. Livestock and grasslands degradation

Grasslands, including rangelands and sown pastures, are among the largest ecosystems in the world

and provide valuable ecosystems services. Grasslands help maintain the composition of the

atmosphere by sequestering carbon, absorbing methane, and reducing emissions of nitrous oxide.

KenyaWorldaverage

KenyaWorldaverage

KenyaWorldaverage

KenyaWorldaverage

Grazing Mixed IndustrialWeightedaverage

Grey 0 106 0 199 0 336 0 219

Blue 97 192 103 241 74 311 101 256

Green 23 139 9 197 6 487 7 348 1 926 4 174 10 171 7 002

0

5 000

10 000

15 000

20 000

25 000m

3 p

er t

on

ne

of

live

anim

al

Green Blue Grey

KenyaWorldaverage

KenyaWorldaverage

KenyaWorldaverage

KenyaWorldaverage

Grazing Mixed Industrial Weighted average

Grey 97 560 53 448 28 254 52 364

Blue 157 563 90 261 47 154 88 234

Green 6 836 6 177 3 750 3 171 1 962 1 823 3 693 2 765

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

m3 p

er t

on

ne

of

live

anim

al

Green Blue Grey

10

Grasslands maintain a large genetic library, they improve regional climate, and preserve soil from

devastating erosion. In many cases, the monetary value of services provided by grasslands in terms

of sustenance of plant and animal life may be larger than the sum of the products with current

market value, such as meat, milk, fibres and hides from ruminant animals. A large fraction of the

grasslands in arid and semiarid environments remain as such and are mainlyutilized to graze

livestock. This is Kenya’s case, where about 60 percent of the livestock herd is found in the arid

and semi-arid lands (ASALs), which is more or less fertile grasslands constituting about 80-84

percent of the country landmass (Barrett et al., 2006; Tully and Shapiro, 2014). The remaining

landmass (16-20 percent) is arable land, where 30 percent are grazing areas for dairy cattle and

commercial hay production (GoK, 2016).

Figure 13. Land use in Kenya

Source: GoK (2016b)

The causes of (grass) land degradation are numerous and complex. Where overgrazing by large

and small ruminants might be the significant one. Overgrazing reduces plant cover, and hence

protection of the soil surface; it causes degradation of riparian woods and forests, at the interface

between land and water sources, with negative effects on water flows; it reduces the population

sizes of wild herbivores and predators, which furthermore affects biodiversity. The overall

consequences are a reduction of provisioning and eco-systems services.

No robust dataset to monitor Kenya’s grasslands status exists, both at national and local level.

However, evidence suggests that growth in both the large and small ruminant population have led

to extension of grazing activity into semi-arid marginal lands and forests, causing soil degradation

and will eventually limit livestock productivity. In pastoral production systems in the ASALs, about

30 percent of the landmass is subject to severe degradation (Mulinge et al., 2016; Waswa, 2012).

Livestock grazing, however, can also support grasslands. As a result of continuous faecal material

dropping by grazing livestock, some semi-arid areas in Kenya with open grazing have more fertile

Water2%

ASAL82%

Commercial Agr -Crop land

5%

Grazing5%

settlemets 3%

Forests3%

Water

ASAL

Commercial Agr -Crop land

Grazing

settlemets

Forests

11

soils than cultivated and fallow lands; due to hoof action of livestock higher bulk density has been

observed in grazing land compared to cultivated and fallow lands (Nyariki, 2016).

4. Livestock and biodiversity loss

Biodiversity, i.e. diversity within and among species and ecosystems, is one of the determinants of

the ecosystem services supply. Changes in biodiversity have important implications on the

ecosystems functions and services to human society. For provisioning services for instance,

intraspecific genetic diversity increases the yield of commercial crops, tree species diversity

enhances production of wood in plantations, and plant species diversity in grasslands enhances the

production of fodder. To regulate processes and services, by increasing plant biodiversity the

resistance to invasion of exotic plants increases, plant pathogens, such as fungal and viral

infections, are less prevalent in more diverse plant communities, plant species diversity increases

aboveground carbon sequestration through enhanced biomass production, and nutrient

mineralization and soil organic matter increase with plant richness (Cardinale et al., 2012).

Despite Kenya being categorized as 80 percent arid and semi-arid, the country hosts a variety of

ecosystems from deserts to savannah grasslands, from dry forests to tropical rainforests and

mangroves. These ecosystems host biological resources of important economic, social and cultural

value (GOK, 2015b). There are nearly 7 500 species of plants in Kenya, including 485 endemic

species; Kenya is the 10th most mammal diverse country in the world with about 375 species

recorded, and ranks 13th in the world for bird diversity, with slightly over 1 000 bird species (Stuart

et al, 1990). However, 30 species of mammals are threatened out of a total of 376, and 41 species

of birds are threatened out of 1 034 (World Data Atlas/Knoema, 2017b).

There is no systematic evidence of the correlation between livestock sector growth with

transformation and biodiversity, but evidence clearly point to a negative relationship. Ogutu et al.

(2016) rely on systematic aerial monitoring survey data collected in rangelands, covering 80 percent

of Kenya’s land surface, to conclude that wildlife numbers declined by 68 percent between 1977

and 2016, while over the same period the livestock population grew. The livestock biomass was

3.5 greater than wildlife in 1977-1980 and 8.1 times greater in 2011-2013. Also, intensification of

livestock in intensive production systems can lead to a decline in biodiversity. Intensive systems

rely on a limited number of animal breeds, although each breed may have a rich genetic

background. A study on breed preference among smallholder dairy farmers in Kenya highlands

reported that 62 percent would prefer raising Friesians, 22 percent Ayrshires and 16 zebus and

their crosses (Bebe et al., 2003). Many animal improvement programmes in Kenya over the past

decades have promoted a shift towards exotic breeds (GoK, 2008). Evidence also indicates that

there could be several positive pathways between livestock, wild animals and other biodiversity

(Riginos et al., 2012; Woldu and Salem, 1999). Even intensive systems may actually protect non-

agricultural biodiversity by reducing pressure to expand crop and pasture areas. In general, the

effect of livestock on biodiversity depends on the magnitude of livestock impacts or the extent to

which biodiversity is exposed to those impacts, how sensitive the biodiversity in question is to

livestock, and how it responds to the impacts (Reid et al., 2009).

12

Box 1. Examples positive livestock-environment interactions

In agropastoral systems, livestock are fed on crop residues and by-products and on pasture. Livestock consecutively return nutrients and organic matter to the soil. In the rangelands of Makueni County, mainly inhabited by agro-pastoralists, a recent study showed that well managed open grazing increases soil fertility. Open grazing lands were found to have a higher cation exchange capacity (CEC), i.e. the capacity of the soil to retain nutrients in plant-available form than cultivated lands and fallow lands. The continuous dropping of faecal material by grazing livestock was a major determinant in fertility difference (Nyariki, 2016).

The production of biogas from livestock dung can be an effective way to control biogas emissions, while at the same time generating energy, which is comparable in ‘cleanness’ to natural gas (Tauseef et al., 2012). In addition, once the manure has been processed through an anaerobic digester and the biogas captured, the remaining liquid effluent can be used as fertilizer. The production of biogas also reduces the need for wood fuel resulting in more trees left standing, which has a positive environmental impact. Biogas technology also generates other benefits, such as reducing atmospheric pollution (smoke from biomass) and the methane emissions from dung left to decompose in an open field (Myles, 2004; IFAD, 2009).

5. Conclusion

While the livestock sector provides a variety of goods and services to society, from food to income

to social functions, its complex interactions with the ecosystem and significant use of natural

resources has wide-reaching implications on society. The sector is the world’s largest user of

agricultural land, considering both grazing and feed-crop land. Kenya’s growing population, rising

incomes and urbanization are translating into an increased demand for livestock products. As a

consequence, the livestock sector will grow and transform, with changed and novel relationships

between livestock and natural resources (FAO, 2009). In Kenya over the last few years, land use

and land cover changes have resulted in social conflicts, decline of wildlife and bird species, loss

in plant biodiversity, and reduced soil productivity.

Assessing the current impact of livestock on the environment it is critical to understand how future

growth and transformation of the sector might impact society, and to identify actions to take today

to ensure a sustainable development trajectory of livestock in the long-term.

This brief summarized evidence of livestock impact on the environment in Kenya. Livestock

negatively impact soil, water and air, thereby degrading the local and global environment, and can

also reduce biodiversity. There is also evidence that increasing resource use efficiency in livestock

systems can generate virtuous circles, in which livestock help maintain a socially desirable natural

environment. It is imperative for policy makers to better appreciate the current connections

between livestock and the environment to anticipate coming challenges and take actions now to

ensure a sector development along with an environmentally sustainable development trajectory.

February 2018. This brief was written by Stephen Gikonyo (FAO), Ana Felis (FAO) and Alessandra

Falcucci (FAO) under the Members of the ASL2050 Kenya Steering Committee’s guidance.

ASL2050 is a USAID-funded policy initiative implemented under the FAO Emerging Pandemic

Threat Programme umbrella.

13

6. References

ASL2050 FAO. 2017a. Africa Sustainable Livestock (ASL) 2050 Kenya country brief. FAO. Nairobi.

From: UN Population Fund data & projections. http://www.unfpa.org/world-population-trends.

ASL2050 FAO. 2017b. Africa Sustainable Livestock (ASL) 2050 Livestock production systems spotlight

Cattle and poultry sectors in Kenya. FAO. Nairobi.

Barrett, C.B., Bellemare, M.F., & Osterloh, Sharon M. 2006. Household-Level Livestock Marketing

Behavior among Northern Kenyan and Southern Ethiopian Pastoralists (November 2004). Available at SSRN:

https://ssrn.com/abstract=716301 or http://dx.doi.org/10.2139/ssrn.716301.

Bebe, B. O., Udo, H. M., Rowlands, G. J., & Thorpe, W. 2003. Small holder dairy systems in Kenya

Highlands; breed preference and breeding practices.

Bebe, B.O., Rademaker, C., Jvan der Lee, J., Kilelu, C., & Tonui, C. 2016. Sustainable growth

of the Kenyan dairy sector – A quick scan of robustness, reliability and resilience.

http://edepot.wur.nl/413390.

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C. & Venail. P. 2012.

Biodiversity loss and its impact on humanity. Nature 486: 59–67. doi:10.1038/nature11148.

Chapagain, A. K. & Hoekstra, A. Y. 2003. Virtual water flows between nations in relation to trade in

livestock and livestock products. Value of Water Research Report Series No 13. Institute for Water

Education, UNESCO-IHE. Delft, Netherlands.

FAO. 2009. The state of food and Agriculture; livestock in the balance.

http://www.fao.org/docrep/012/i0680e/i0680e.pdf.

FAO. 2016a. Global Livestock Environmental Assessment Model; Reference documentation Version 2.0.

FAO. 2016b. AQUASTAT website. Food and Agriculture Organization of the United Nations

(FAO).

FAO. 2017a. Livestock and the environment. www.fao.org/ag/againfo/themes/en/Environment.html

FAO. 2017b. Global Livestock Environmental Assessment Model (GLEAM). www.fao.org/gleam/en/

FAOSTAT 2017. Kenya, Live Animals, Cattle, Sheep, Goats and Camels, viewed on March 30 2017.

GoK, 2008. Sessional paper No. 2 of 2008; National Livestock Policy, Ministry of Livestock Development.

Government of Kenya, Nairobi.

GoK, 2015a. Kenya’s Intended Nationally Determined Contribution (INDC) 23 July 2015. Ministry of

Environment and Natural Resources Government of Kenya. Nairobi.

www4.unfccc.int/ndcregistry/PublishedDocuments/Kenya First/Kenya_NDC_20150723.pdf

GOK, 2015b. Second National Communication to the United Nations Framework Convention on Climate

Change. First published 2015: National Environment Management Authority, Government of

Kenya.

GoK, 2015c. Water permit database. Water Resources Management Authority. Government of

Kenya.

GOK, 2016. Agricultural Policy- Food: Our Health, Wealth and Security: Ministry of Agriculture

Livestock and Fisheries. Government of Kenya, Nairobi.

http://www.fao.org/gleam/en/

14

International Fund for Agricultural Development (IFAD), 2009. (Draft). Comprehensive Report

on IFAD’s Response to Climate Change through Support to Adaptation and Related Actions.

IPCC (Intergovernmental Panel on Climate Change). 2014. Climate Change 2014: Mitigation of

Climate Change. Cambridge University Press. www.ipcc.ch/pdf/assessment-

report/ar5/wg3/WGIIIAR5_SPM_TS_Volume.pdf

Lardy, G. Stoltenhow, C. & Johnson, R. 2008. Livestock and Water. North Dakota State

University, Fargo, North Dakota.

Maitima, J. M., Mugatha, S. M., Reid, R. S., Gachimbi, L. N., Majule, A., & Lyaruu, H.

2009. The linkages between land use change, land degradation and biodiversity across East Africa. African

Journal of Environmental Science and Technology, 3(10), 310–325.

Mekonnen, M.M. & Hoekstra, A.Y. 2010. The green, blue and grey water footprint of farm animals and

animal products. Value of Water Research Report Series No. 48, UNESCO-IHE, Delft, the

Netherlands.

Mekonnen, M. M., & Hoekstra, A. Y. 2012. A global assessment of the water footprint of farm animal

products. Ecosystems, 15(3), 401-415.

Monfreda, C., Ramankutty, N. & Foley, J.A. 2008. Farming the planet: 2. Geographic distribution of

crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical

Cycles, 22: GB1022.

ftp://ftp.ciat.cgiar.org/DAPA/projects/Cursos_Talleres/EcoCrop/_documents/Monfreda_et_

al_2008_Farming-the-planet.pdf

Mulinge et al. 2016. Economics of Land Degradation and Improvement in Kenya.

https://www.researchgate.net/publication/286744415.

Myles, R. 2004. Environmental, Social and other Positive Impacts of Building Household Biogas Plants in

Rural India. A paper prepared for (World-Councilfor-Renewable-Energy) and EUROSOLAR

sponsored Second World Renewable 99 Energy Forum: Renewing Civilization by Renewable

Energy, from May 29 - 31, 2004, Bonn, Germany.

Nyariki, D. 2016. Rangelands in semi-arid Kenya have recently witnessed extensive land use changes.

http://hdl.handle.net/123456789/71.

Ogutu, J. O., Piepho, H. P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C. &

Wargute, P. W. 2016. Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What

are the causes? PloS one, 11(9), e0163249.

Peden, D., Tadesse, G., Mammo, M. 2002. Improving the water productivity of livestock: An opportunity

for poverty reduction.

www.ilri.org/publications/cdrom/integratedwater/iwmi/Documents/Papers/Don.htm

Ramankutty, N., Evan, A.T., Monfreda, C. & Foley, J.A. 2008. Farming the planet: 1. Geographic

distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22, GB1003.

Reid, R. S., Nkedianye, D., Said, M. Y., Kaelo, D., Neselle, M., Makui, O., ... & Ogutu, J.

2016. Evolution of models to support community and policy action with science: Balancing pastoral livelihoods and

wildlife conservation in savannas of East Africa. Proceedings of the National Academy of Sciences,

113(17), 4579-4584. https://doi.org/10.1073/pnas.0900313106.

https://www.researchgate.net/publication/286744415.

https://doi.org/10.1073/pnas.0900313106

15

Riginos, C., Porensky, L. M., Veblen, K. E., Odadi, W. O., Sensenig, R. L., Kimuyu, D., ...

& Young, T. P. 2012. Lessons on the relationship between livestock husbandry and biodiversity from the Kenya

Long-term Exclosure Experiment (KLEE). Pastoralism: Research, Policy and Practice, 2(1), 10.

https://pastoralismjournal.springeropen.com/articles/10.1186/2041-7136-2-10

Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M. & de Haan, C. 2006.

Livestock´s long shadow. Environmental issues and options. FAO. Rome, Italy.

Stuart, S.N., Adams, R.J & Jenkins, M.D., 1990. Biodiversity in Sub- Saharan Africa and its Islands:

Occasional papers of the IUCN Species Survival Conservation, No.6.

Tauseef, S.M., Premalatha, M, M, M. & Abbasi S.A. 2012. Methane capture from livestock manure.

Journal of Environmental Management Volume 117, 15 March 2013, Pages-187 207

https://doi.org/10.1016/j.jenvman.2012.12.022.

Tully, T.G. & Shapiro, E. 2014. Analysis of the Kenyan Livestock Market and Feasibility Study of a

Livestock Business.

https://pdfs.semanticscholar.org/0498/3dea2d4a8f59c5d759324029bd1915decd2f.pdf

Waswa, B. S. 2012. Assessment of Land Degradation Patterns in Western Kenya: Implications for Restoration

and Rehabilitation. ZEF. University of Bonn, Germany.

Woldu, Z., & Saleem, M. M. 2000. Grazing induced biodiversity in the highland ecozone of East Africa.

Agriculture, Ecosystems & Environment, 79(1), 43-52.

World Bank. 2010. World Development Indicators: 2010. 1818 H Street NW, Washington, D.C. 20433

USA, 2010.

World Data Atlas/Knoema. 2017a. Kenya - Emissions - Agricultural nitrous oxide emissions as a share

of total emissions. https://knoema.com/atlas/Kenya/topics/Environment/Emissions/CO2-

emissions.

World Data Atlas/Knoema. 2017b. Kenya Biodiversity and Protected areas.

https://knoema.com/atlas/Kenya/topics/Environment#Biodiversity-and-Protected-Areas.

WRI CAIT 2.0, 2017. Greenhouse Gas Emissions in Kenya. Kenya Numbers at a Glance (2013.-

2017_USAID_GHG Emissions Factsheet_Kenya.pdf.

https://knoema.com/atlas/Kenya/topics/Environment#Biodiversity-and-Protected-Areas

16

Annex 1

Table A1. Kenya: Dairy Production Systems

Animal population: 4.5 million dairy cows

Intensive (zero grazing) dairy system

In intensive dairy systems, farmers keep stall fed exotic cattle and sell most of the milk produced to the market. While the average herd size varies, in most cases farmers keep few dairy animals and also a few acres of land, which allows a close livestock-crop (mainly maize) integration. Intensive dairy farms are concentrated in the mid- and high-altitude agroecological zones, where cereal and cash crops are grown. In particular, they are predominant in Mount Kenya and central Rift Valley regions, and present in many urban and peri-urban zones in humid and sub-humid areas of the country.

Semi-intensive (semi-grazing)

This is the most popular dairy system. Farmers let the animal graze at daytime and provide feeding at night, including supplements during milking. Farmers keep crosses of the dairy breed of cattle, and dairy animals are part of a larger herd, including other animals such as chicken, sheep, goats, donkeys and, occasionally, pigs. Semi-intensive dairy systems are concentrated in Mount Kenya, the central and north Rift Valley and in the coastal regions, but present in all areas where crop farming is practiced, such as the western and Nyanza regions.

Extensive (controlled and uncontrolled)

This is a pasture/based production system and practiced in large farms (controlled grazing with large herds) and in marginal and communal grazing lands (uncontrolled grazing smaller herds). Animals are both exotic and improved. Under controlled grazing, animals are placed on natural and improved pastures using paddocks or strip grazing and supplemented with high quality fodder, mineral licks and commercial concentrates. Uncontrolled grazing is characterized by free grazing in natural pastures. This system is found in North and South Rift Valley, Eastern and Coast Regions.

Source: ASL2050 FAO (2017b)

Table A2. Kenya: Beef Production Systems

Animal population: 14 million beef cattle

Pastoralism (extensive system)

Pastoralism is a low-input low-output subsistence system, with indigenous cattle relying entirely on communal grazing areas and water sources. Pastoralism, including transhumance and nomadic pastoralism, is practices in arid and semi-arid areas. Livestock density in pastoral areas is low, at about 11 Tropical Livestock Unit.

Ranching (extensive)

Ranches are made up of large land areas and have large herds, including local, crossed and exotic breeds. Most ranches have infrastructure for disease control, feeding and water storage. It is a highly commercial system targeting prime local niche and export markets and, as such, contributes to the Kenya export revenue.

Agro pastoralism

Agro-pastoralists keep a mixed herd, including beef cattle, and feed animals with crop residues and other products. They make use of animal manure and

17

(semi-intensive)

draft power to increase crop productivity. Agro-pastoralism is a low input low output system, subsistence oriented, and mainly practiced in semi-arid areas. Animal densities range from 20 tropical livestock unit per km2 in the lowlands to 50 in the highlands.

Feed lot (intensive)

This is a commercially-oriented system in which animals are kept for a short period (about 3 months) during which they are fattened and sold to niche/prime beef markets. It is both a capital and labour-intensive system, with significant investments in feeding and animal health. There are two different feed lot systems – one focusing on fattening dairy culls and dairy bull calves, the other fattening beef breeds.


Table A3. Kenya: Poultry Production Systems

Bird population: 39 million meat or dual purpose chickens

Intensive production system (broiler farming)

Broiler farming in Kenya is practised in urban and peri-urban areas, such as around Nairobi, Mombasa, Nakuru and Kisumu. This system requires little space and exotic birds – mainly sourced locally or imported from Uganda – are kept in large hangars and fed compounded feed. This system is market-oriented. It is estimated that over 3 million broiler chickens are raised in Kenya, including in small, medium and large farms. Flock sizes per cycle vary from 50–500 (small scale) through 500-10 000 (medium) to over 10 000 (large and integrated farms). The system is market-oriented.

Semi-intensive production system

Farmers keep flocks of 30 to 100 birds confined simple. Birds are either indigenous or exotic, and provided with feed supplements. Farmers sell most of birds, though some are self-consumed. Semi-intensive production system is practiced throughout the country, but the exact number of semi-intensive farms is not known, though experts estimate they can keep up to a third of all chickens in the country.

Extensive system (free-range)

This is a low input low output system where birds are left to freely roam for feed. Farmers keep flock ranging from 5 to 30 local birds, often manged by women and children. It is a subsistence-oriented system, with little and opportunistic informal marketing. Although popular throughout the country free ranging is predominant in western Kenya regions, some parts of lower eastern, north Rift areas and coastal areas.


18

Annex 2

Table A4. Kenya: emissions from poultry production systems (tonnes of CO2 eq/year).

Indigenous systems Broilers

Emission source Semi-intensive Free range Intensive Small Scale

Intensive Medium Scale

Intensive Large Scale

Total

Fertilizer 2 720 3 026 5 882 2 102 769 14 498

Applied manure 4 592 4 816 11 259 4 023 1 471 26 161

Crop residues 3 320 4 065 5 495 1 963 718 15 561

Feed: rice 387 616 - - -

Feed 10 994 5 922 52 340 18 702 6 841 94 798

Land use change 1 541 - 11 096 3 965 1 450 18 052

Manure management (CO2 eq fromCH4)

2 961 4 362 1 572 562 205 9 661

Manure management (CO2 eq from N2O)

29 320 45 392 5 576 1 992 729 83 009

TOTAL CO2 eq. 55 835 68 199 93 219 33 308 12 183 262 744

Tonnes of CO2 eq / year / per head

0.004 0.004 0.017 0.017 0.017


Table A5. Kenya: emissions from dairy production systems (tonnes of CO2 eq/year).

Emission source Large Scale Intensive

Small Scale Intensive

Semi- intensive

Extensive (controlled)

Extensive (uncontrolled)

Total

Enteric fermentation 415 805 2 133 586 2 407 261 332 458 297 764 5 586 873

Manure management (CO2 eq from CH4)

172 825 886 802 76 054 8 240 7 380 1 151 301


53 653 275 305 43 142 8 661 7 757 388 517

Feed emission (CO2 eq from N2O)

280 1 436 2 194 512 459 4 880

Feed emission CO2 8 392 43 062 26 748 37 33 78 272

TOTAL CO2 eq. 650 955 3 340 190 2 555 399 349 908 313 392 7 209 844

Tonnes CO2 eq/year/per head

2.15 2.15 1.19 1.31 1.31


19

Table A6. Kenya: emissions from beef production systems (tonnes of CO2 eq/year).

Emission source Extensive Pastoralism

Semi- Intensive

Extensive Ranching

Feed Lot (Intensive)

Total

Enteric fermentation 8 627 582 6 221 205 813 712 55 581 15 718 080

Manure management (CO2 eq from CH4)

302 176 258 653 28 500 1 395 590 724


538 108 463 416 50 752 7 325 1 059 600

Feed emission (CO2 eq from N2O)

1 837 230 802 747 173 279 15 782 2 829 038

Feed emission CO2 109 438 184 804 10 322 6 311 310 875

TOTAL CO2 eq. 11 414 533 7 930 825 1 076 564 86 394 20 508 317

Tonnes of CO2 eq/year/per head 1.412 1.463 1.412 2.010


© FAO, 2018I8973EN/1/04.18