Mohammed Y Said, Leah Ng'ang'a, Gert Nyberg, Shem Kifugo, Ewa
Wredle, Anna Hallmén, Regina Waiganjo, Peter Mwangi, Jan de Leeuw
and Polly Ericksen
Measuring resilience—Understanding trends in land cover
changes and their potential impacts on pastoral communities
IFPRI 2020 Policy Consultation and Conference, Side Event on Measuring and Evaluating
Resilience in Drylands of East Africa, Addis Ababa, 15-17 May 2014

Resilience framework
Source: Pasteur, K. 2012. From Vulnerability to Resilience A framework for analysis
and action to build community resilience
“ … measure of the persistence of systems and of their ability to absorb change and disturbance and still
maintain the same relationships between populations or state variables (Holling, 173)
Future uncertainty
(Long term trends, climate change)
Adaptive capacity
• Improving understanding of trends and
local impacts
• Ensuring access to relevant and timely
information
• Building confidence and flexibility to
learn and experiment
Governance
Enabling environment
• Decentralized and participatory decision
making
• Strengthening links between local
district and national levels
• Promoting integrated approaches to
livelihoods, disaster and climate change
• Addressing underlying systematic issues
Resilience
• Ability to manage
risk
• Ability to adapt to
change
• Ability to secure
sufficient food
Hazards and stresses
Disaster preparedness
• Building capacity to analyse hazards
and stresses
• Improving hazard prevention and
protection
• Increase early warning and awareness
• Establishing contingency and
emergency planning
Livelihoods
Diversity and security
• Strengthening community organization
and voice
• Supporting access to, and sustainable
management of productive assets
• Promoting access to technologies
• Improving access to markets and
employment
• Ensuring secure living conditions

Projected population and water use
Source: ACC 2014 Natural Capital Atlas
In 1960 Kenya population was less
than 10 million by 2009 the had
increased to 40 million. This
population will double to 80 million
by 2050
Growing human population and rise
per capita use of resources is
depleting water supply in Kenya.
High conservation and management
will be needed to deal with water
shortages now and in the future.

Study sites – agro ecological potential
Source: KNBS, KSS, ILRI
1
32
6
54
7
8
1 = West Pokot; 2 = Elgeyo Marakwet; 3 = Baringo; 4 = Nyandarua; 5: Nyeri
6 = Narok; 7 Machakos; 8 = Kwale
Human Population
1962
2009

Causes of forest cover changes
Underlying cause
Source: Geist & Lambin 2002

Population dynamics
y = 0.0018x - 0.3671
R² = 0.8088
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0 500 1000 1500 2000
log(Populationdensity)
Rainfall (mm)
1962
2009
Linear (1962)
Linear (2009)
0
500000
1000000
1500000
2000000
2500000
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
Population
Baringo
Elgeyo-Marakwet
Kwale
Machakos
Narok
Nyandarua
Nyeri
West Pokot
Source: Said et al. (in prep)

Monitoring Vegetation condition using satellite
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Ndvi
Years
Baringo
Elgeyo-Marakwet
Kwale
Machakos
Narok
Nyandarua
Nyeri
West Pokot
Source: Said et al. (in prep)
Trends in NDVI for the period 1982 -2008. A 3 year smoothing
interval used to clearly show the trends

Framework - Relationship between population
growth and vegetation
Influence of population growth on vegetation cover can be considered as two effects – consuming
constructive effect and constructive effect.
C = Consuming Destruction Effect
D = Planting Construction Effect
Population density
Vegetationcover
C>DD>C
Population density
Vegetationcover
D>C C>D
Population density
Vegetationcover
C>D D>C
Source: Li et al. 2013, Modified

Land cover changes Mau - Narok
y = -7E-08x + 0.5383
R² = 0.4168
0.47
0.48
0.49
0.50
0.51
0.52
0.53
0.54
200000 400000 600000 800000
NDVI
Population
Source: Said et al. (in prep), Leah (in prep), Hansen et al. 2013)
2001 2012 Change analysis 2001 -12

Impacts on livelihood and environment – southern Kenya
rangelands
0
500000
1000000
1500000
2000000
Jan-77
Jan-80
Jan-83
Jan-86
Jan-89
Jan-92
Jan-95
Jan-98
Jan-01
Jan-04
Jan-07
Jan-10
Populationsize
Kajiado
Shoats
Cattle
Wildliffe
0
500000
1000000
1500000
2000000
2500000
Jan-77
Jan-80
Jan-83
Jan-86
Jan-89
Jan-92
Jan-95
Jan-98
Jan-01
Jan-04
Jan-07
Jan-10
Populationsize
Narok
Shoats
Cattle
Wildlife
Ogutu, Said, Kifugo in press

y = 4E-12x2 - 4E-06x + 1.9121
R² = 0.3043
0.57
0.58
0.59
0.60
0.61
0.62
0.63
0.64
0.65
0.66
0.67
0.68
500000 550000 600000 650000 700000
NDVI
Population
Land cover Mt Kenya - Nyeri
Source: Said et al. (in prep)
2001 2012 Change analysis 2001 -12

Framework - drylands

1983
2013
Source: Anna Hallmén, MSc Thesis 2014
Photos: Vi
Image showing changes in land cover – in 2013 the
red tone colour indicates increase in tree cover
Efforts to increase tree cover started in the early 1980s
by Vi Sweden in West Pokot. It started in schools and
churches and later extended to communal and private
lands.
Land cover changes in West Pokot 1983 -2013

Impacts of enclosures on vegetation
Chepareria
y = 1E-07x + 0.4672
R² = 0.6804
0.48
0.49
0.50
0.51
0.52
0.53
0.54
0.55
100000 200000 300000 400000 500000
NDVI
Population
81%
51%
0%
20%
40%
60%
80%
100%
enclosure open
Totalplantcover
0
5
10
15
20
25
30
35
closed open
Foragebiomass(g/m2)
0
2
4
6
8
10
12
14
closed openSpeciesrichness
Source: Google maps
Source: Regina Waiganjo, MSc Thesis 2014;
Said et al. (in prep)
Enclosures had more plant cover, biomass was four folds and species richness higher than the open grazing
areas

Next steps
Socio - ecological changes
• What are the land cover dynamics
and trajectories in the drylands of
EA?
• What correlation can be established
between climatic indicators and
vegetation indexes?
• What type land health can we detect
• Potential for PES?
Livelihood challenges and
opportunities
• Does the number of livestock,
stocking rate and composition differ
between pastoralists using enclosures
compared with traditional nomadic
pastoralists?
• Have a more sedentary lifestyle
changed the roles in the family and
the women’s economic
empowerment?
• What are the trades-offs between
sedentary and non-sedentary
population?

Acknowledgement
Projects or programs: CRP 1.1, BMZ (Developing the livelihood income
diversification potential of carbon sequestration in African drylands), Triple L
(Students and Scientists)
Data: Department of Resource Surveys and Remote Sensing (DRSRS), Kenya
National Bureau of Statistics (KNBS), Kenya Agriculture Research Institute - Kenya
Soils Surveys (KSS), Vi West Pokot, NOAA, and Google.