The nexus between land cover changes, politics and conflict in Eastern Mau forest complex, Kenya

Applied Geography 114 (2020) 102115

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The nexus between land cover changes, politics and conflict in Eastern Mau forest complex, Kenya R.M. Kweyu a, *, T. Thenya b, K. Kiemo c, J. Emborg d a

Department of Geography, Kenyatta University, Nairobi, Kenya Department of Geography & Environmental Studies, University of Nairobi, Kenya c Department of Sociology, University of Nairobi, Kenya d Department of Food and Resource Economics, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark b

A R T I C L E I N F O

A B S T R A C T

Keywords: Land cover changes Resource conflict Political ecology Forest policy

There is growing literature that links land cover changes to resource governance regimes. Whereas natural resource degradation has been successfully linked to weak governance, the reciprocal relationship between degradation and conflict has not been clearly established especially in sub-Saharan region where natural resource conflicts are common. This paper utilizes remote sensing and spatial techniques to examine land cover changes and conflict in light of the changing Kenyan policy and political contexts. The paper draws evidence from data collected through time series of satellite imagery for Eastern Mau forest complex between 1976 and 2014 and qualitative data including key informant interviews and observation through geo-coded transect walks. The changes in land cover and conflict intractability were analyzed in light of post-independence land use policy history of Kenya and related to conflict occurrences among Eastern Mau forest adjacent communities. The study results show that between 1976 and 2014 over 40% of forest land was converted to other uses. The study also documents both spatio-temporal drivers of conflict (e.g. forest degradation) and drivers related to political practice and competition among ethnic groupings. This paper concludes that to stem land cover changes there is need to pay greater attention to the underlying factors to land cover changes such as conflict, policy and politics.

1. Introduction Land cover changes are as result of complex interaction with greater focus being on proximate causes such as farming and logging in tropical Africa (Mertens & Lambin, 2000). According to Walker and Homma (1996), development of households’ strategic responses to macro-economic and political conditions can lead to changes in farming systems, which in turn affects land-use and land-cover changes. The land changes can affect household economic struggle, which sometimes are associated with conflict over land resource access and control. Agricul­ tural activities especially demand for farming land is one of the main dynamic causes of land cover change (Geist & Lambin, 2002; Manh Vu, Le, Frossard, & Vlek, 2014; Miyamoto, Parid, Aini, & Michinaka, 2014; Thenya & Ngecu, 2017). Access to farming land and settlement is driven by a combination of factors among them economic, social instability and politics. In all parts of the world access to land and natural resources has been a major source of conflict (Alao, 2007; van Leeuwen, 2010). This often

leads to uncontrolled use of resources that can lead to deforestation and degradation. Land cover changes such as deforestation have often been related to community instability and changing economic dynamics. Studies on land use changes have focused mainly on ecological aspects, policy and utilization as drivers of land cover change (Geist & Lambin, 2002; Govindaprasad & Manikandan, 2016; Manh Vu et al., 2014). Thus, there is little focus on conflict as underlying drivers to land cover change. Analysis of conflict as a driver of land cover change can help uncover the pattern and predict effect of primary activities such as politics and policy. Understanding the pattern and dynamics of conflict could help in managing land cover change in conflict prone zones. This paper spe­ cifically focuses on conflict as a driver of land cover change in East Mau forest complex in Kenya by exploring two research questions: 1. How has land cover changed in the area from 1976 to 2014? 2. Which forest related conflicts have occurred in the area?

* Corresponding author. Geography Department Kenyatta University, P.O Box 43844 00100, Nairobi, Kenya. E-mail address: [email protected] (R.M. Kweyu). https://doi.org/10.1016/j.apgeog.2019.102115 Received 5 September 2018; Received in revised form 9 November 2019; Accepted 11 November 2019 Available online 29 November 2019 0143-6228/© 2019 Elsevier Ltd. All rights reserved.

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The conflict land-cover change nexus is discussed against the back­ drop of other underlying factors such as politics, legislation, governance and patronage in Eastern Mau. In addition the study takes an interdis­ ciplinary approach, based on empirical experiences drawn from Eastern Mau forest complex in Kenya. Interdisciplinary approaches are very useful as they allow a combination of social and natural sciences (Manh Vu et al., 2014; Rindfuss, Walsh, Turner, Fox, & Mishra, 2004), thus allowing great understanding of the relationship between socio-economic factors like conflicts and land cover change. The purpose of this study is to help provide empirical evidence of the relationship between conflict and land cover change in forested ecosystem in a country where natural resource conflicts are common. This work does so by using spatial techniques to map forest resource change and links this to occurrence of ethnic conflict. By so doing, the study aims at contributing to the growing body of knowledge on the application of GIScience in solving societal problems.

Market failures (externalities), government failures (environmentally adverse policies), population growth and property rights failures (Pearce & Turner, 1991; Heltberg, 2002). In the Kenyan case and especially within the Eastern Mau, two factors in particular, seem to be at play: Firstly, government failure as manifested through weak forest legislation that permitted forest excision by a single government minister without consultation with other stakeholders (Guthiga, Nyangena, Juma, & Sikei, 2014). The second factor was the property rights failure as not all land was registered after forest conversion resulting in self allocation and extraction of resources like timber in free for all scenario (Guthiga et al., 2014). These two factors easily triggered intermittent conflicts and political patronage in competition for resources, which further destabilized resource governance hence resulting into degradation. Such evidence from existing studies stimulates our attempt to reach a deeper understanding of the role of land use and cover change as a driver of conflict. 2. Study area

1.1. Context, theory and conceptual framework

The Eastern Mau forest block is one of the two largest forest blocks in the Mau Complex, covering about 66,000 ha (out of which 35,301 ha were excised in the year 2001 for settlements) (GOK, 2009; UNDP, 2008) (Fig. 1). The area is characterized by cool and wet climate. The area has deep volcanic soils that are highly fertile making the area attractive to a wide range of agricultural practices. The Eastern Mau landscape is mainly used for growing crops such as maize, potatoes, peas and wheat as well as grazing of cattle and shoats. In the Mau, economic activities such as farming and illegal logging compete for land with forestry activities, leading to deforestation. The Eastern Mau highlands have been heavily logged leading to forest destruction, mainly targeting hardwood species such as Parasol Tree (Polyscias kikuyuensis Summerh),

The question of sustainable use of natural resources has been around for centuries, and is reflected in Boserupian demographic theory (Carr, 2004). Boserup approach is optimistic that use of resources does not necessarily lead to degradation when policy support is adequate, tech­ nology is available and governance is enhanced (Demont et al., 2007). The sustainable use of natural resources depends on population dy­ namics (particularly density, and growth rate) as well as governance system. According to the theory by Boserup (2005), increased popula­ tion has a trigger on diversification in production systems including enhanced legislation, skilled workforce, high quality seeds, machinery and fertilizers among other aspects. Scholars often distinguish among four main underlying factors that cause environmental destruction:

Fig. 1. The study area-Eastern Mau (satellite imagery obtained from USGS, 2014). 2

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red cedar (Juniperus virginiana L.) and Podocarpus–Olea (Birdlife Inter­ national, 2013). The Mau catchment provides both environmental and economic services to Kenya and the region by supporting different sectors including energy, tourism, agriculture and domestic water supply (UNDP, 2008). The market value of electricity generated from hydro­ power plants on rivers originating from the Mau complex have been projected at Kshs 5.3 billion annually (Kipkoech, Mogaka, Cheboiywo, & Kimaro, 2011). The catchment area through its supply of water to wildlife sites and Important Bird Areas (IBAs), has contributed to tourism development in Kenya and Tanzania. By regulating the climate of the surrounding areas, the catchment supports agricultural produc­ tion (through growing of cash crops such as tea, rice, wheat, barley, pyrethrum; subsistence crops; and livestock). These benefits among many others make the Mau forests complex an important catchment and conservation area.

conflict for instance, government and non-governmental organizations working towards peace and/or conservation of the forest. Others were law enforcers like police officers, community elders and Community Forest Associations (CFA) leaders. 3.3. Geo-coded transect walks Community participatory mapping was done with the help of vil­ lagers in the study area. Participants were selected based on desired qualities such as involvement in village leadership, longevity in the village, women representatives, involvement in community activities (such as Community Forest Associations) and willingness and avail­ ability as suggested by Shrestha (2006). The transect walks were carried out across both forest land and settled areas adjacent to the forest. The spatial data collected along the transects included conflict hotspots such as water points, grazing areas, access road, disputed farming areas and deforested areas.

3. Materials and methods

3.4. Integrating land cover and geo-coded transect walk data

3.1. Spatial analysis – satellite image analysis

The transect walk data was imported into ArcGIS 12.0 and overlaid on the land cover to generate a map of conflict spots. Information generated from KIIs was also integrated at this stage. The land cover map generated from 2014 image was overlaid with the administrative Lo­ cations map and the resulting coverage integrated with GPS data collected during the transect walks. This integration of land cover and transect walks allowed analysis of the relationship between conflict zones and change in land cover.

In this study comparative land cover changes analysis was under­ taken using satellite images covering the dry weather period between 1976 and 2014. The specific images used were Multi-spectral scanner (MSS) of 25th January 1976, Thematic Mapper (TM) of 28th and 21st January 1986 and 1995 respectively and Enhanced Thematic Mapper (ETM) of 4th February 2003 and Operational Land imager of 25th January 2014 all obtained from US Geological Survey (USGS, 2014). This comparative approach using same season imagery is common in land-use change analysis since spectral characteristics of features may change over time based on weather variations (Rawat & Kumar, 2015). In order to facilitate comparison between the MSS (image pixel of 60 m) and the TM and ETM images with images pixel of 30 m, the later (TM and ETM) were degraded to 60 m to allow comparison in ERDAS using the nearest neighbor re-sampling technique. This approach is frequently used to facilitate comparison of satellite images with different spatial resolution (Benson & Mackenzie, 1995; Saura, 2004; Thenya & Ngecu, 2017; Turner, O’Neill, Gardner, & Milne, 1989; Wu, Jelinski, Luck, & Tueller, 2002). Initially the images were subjected to radiometric cor­ rections and then registered to each other. This was followed by geo-rectification, after which images layer stacking was undertaken before finally sub-setting the study area. To ensure effective image analysis, hybrid land cover classification was done based on both unsupervised and supervised procedures. Un­ supervised classification was used in the preliminary classification where iso-cluster algorithm was used to group image pixels into different clusters. Different land cover classes were assigned to the different clusters based on the interpreter knowledge. Fifty (50) random sample points were generated on the classified images and their ground loca­ tions visited using a handheld GPS unit for ground truthing by observing and identifying the exact land cover classes that corresponded to the different clusters. The identified classes were used to generate different land cover signatures that were used to run supervised classification based on maximum likelihood (maxlike) algorithm to get the final classification. Accuracy assessment was done based on the comparison between preliminary and final classification. Land use change matrices were generated between different final land cover classifications generated for different years. The confusion matrix indicated a high accuracy percentage for all classes ranging between 90 and 94%.

4. Results 4.1. Land cover changes in Eastern Mau The study area experienced varying changes in land cover between 1976 and 2014. Overall, the forests cover reduced from 54,535.25 ha in 1976 to 31,122.78 ha in 2014 that is 43.5% reduction. During this period indigenous forest lost 6,041 ha, with major losses in 1995.It is notable that 90% of this (5,447 ha) was converted to plantation forest. On the other hand, cultivated land increased progressively reaching a high peak in 2014 (Fig. 2). Apart from forested areas, the shrubs reduced from 26,459.87 ha in 1976, to 5,385.803 ha in 2014 (Table 1). The cultivated fields increased from 30,716 ha (accounting for 25% of the study area in 1976) to 81,167 ha (accounting for 65% of the study area forest cover in 2014), which represent an increase of 50,451 ha or 40% of the study area. 4.2. Intra decade change detection and land cover dynamics 4.2.1. 1976–1986 period During this period, the most significant conversions were from nat­ ural vegetation to cultivated fields. Shrubs were the most affected of the vegetation types. The area under shrubs decreased from 26,456.04 ha–8984.88 ha. This means that shrubs contributed about 17,955 ha to conversions followed by grassland at 847.08 ha, with indigenous forest contributing only about 151 ha (Table 1). Shrubs are often at low altitudes and are easy to access. Most of the conversions occurred in the western side of the study area around Lare region (Fig. 3). Lare, owing to its proximity to Nakuru town may have attracted settlements faster than other regions. Also noted during this epoch was increase of plantation forest from 16737 ha to 22197 ha. This trend points to some deliberate efforts to increase tree cover through Cypressus lusitanica and Pinus patula forests under plantation establish­ ment, also dubbed the Shamba system.

3.2. Key informant interviews In order to gather useful information on spatial and temporal di­ mensions of conflict, data was collected through fifty five (55) key informant interviews (KII’s) selected through snowballing. The key in­ formants involved parties who had influence in changing patterns of

4.2.2. 1986 and 1995 period Compared with the previous period, minimal land cover changes 3

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Fig. 2. Land cover/use changes between 1976 and 2014. Table 1 Land use change matrix for 1976 and 1986 (60 m pixel) in ha. Year

1986

1976

Land Cover Class Acacia Bare ground Built up area Grassland Shrubs Lake Cultivated fields Plantation forest Indigenous forest Total (Ha)

Acacia

Built up area 0 0 108.36 0 10.08 0 164.52 6.48 0

Grassland

Shrubs

Lake

588.96 0 0 33.48 6.12 0 0 0 0

Bare ground 0 109.08 0 37.08 8.28 118.44 0 0 0

304.2 516.96 1888.2

0 0 0 0 0 677.16 0 0 0

Cultivated fields 0 0 0 847.08 17955 0 29853 50.76 151.92

Plantation forest 0 0 0 1113.84 3276 0 312.84 13589.64 3904.92

Indigenous forest 0 0 0 169.56 786.96 0 0 83.16 30717.72

Total (Ha) 591.12 110.88 108.36 12065.4 26456.04 795.6 30719.16 16737.84 37799.28

0 0 0 7173.72 832.68 0 84.6 2490.84 1136.52

2.16 1.8 0 2690.64 3580.92

628.56

272.88

289.44

11718.36

8984.88

677.16

48857.76

22197.24

31757.4

125384

Although the government excisions were reported to have been stopped in the year 2001, satellite imagery analysis indicated continued forest degradation which could be attributed to illegal excisions and encroachment. The period running between 2003 and 2014 recorded an increase in the violent ethnic clashes. Some of the clashes were directly linked to the forest resource competition such as land clashes in Likia area and pasture scramble in Longoman forest.

were observed between 1985 and 1995. The changes were recorded in the grassland, cultivated fields, indigenous forests and shrubs cover. Indigenous forest coverage increased at the expense of shrubs (1069.2 ha) and grassland (672.84 ha) (Table 2). This trend can be explained by natural regeneration of indigenous tree species in uncul­ tivated lands after the removal of workers from the forest. The govern­ ment forest management regulation had changed from residential forest establishment to non-residential cultivation (NRC) under Shamba sys­ tem. Towards the end of 1986–1995, the first major inter-ethnic clashes occurred around Lare division.

4.3. Social analysis Information from key informant interviews and geo-coded transect walks indicated that human activities pioneered by settlements started intensifying in Eastern Mau during the period between 1995 and 2003. The members of the Ogiek and Maasai communities were the first to settle within Eastern Mau, followed by the Kikuyu and Kalenjin com­ munities. Although different respondents presented different and often contradicting accounts of community migration into Eastern Mau, it was evident during the field study that the Kikuyu, Ogiek, Maasai and Kalenjin lived peacefully side by side in the lands bordering the forest in Nakuru County hitherto 1990s. Apart from the four main communities, other ethnic groups such as the Kisii, Luhyia and Kamba also settled in the Eastern Mau forest although in small numbers.

4.2.3. 1995–2003 period This epoch was characterized by major conversions of forest into cultivated fields in comparison to the previous periods. Cultivated land increased from 50492.16 ha in 1995–74362.32 ha in 2003 (Table 3). The increase was at the expense of plantation forest (13633.92 ha), indigenous forest (5136.12 ha), grassland (3823.2 ha) and shrubs (1695.96 ha). The expansion of cultivated fields into forest land points to an in­ crease in human activity and encroachment in the forest reserves (Fig. 4). This epoch coincided with the advent of multi-party politics in Kenya which led to increased political activities including settling of potential voters on the excised forest land. Consequently, Eastern Mau region witnessed increased incidences of violence involving different ethnic groups as well natural resource competitions.

4.3.1. Hotspots of conflict and deforestation During the transect walks, several sites were identified that were hotspots of conflict and also associated with deforestation. The main genesis of these conflicts was increased migration attracted by excisions of forest land in the 1990’s. The excisions were politically driven and resulted in allocation of forest land to perceived potential voters. These led to resource competitions and ethnic clashes. The clashes persisted through every national election period and reached their peak in 2007/ 2008. One of the forest blocks that experienced degradation and conflict

4.2.4. 2003 and 2014 period A reduction in forest cover was observed in this epoch but at a lower rate compared to the previous period (1995–2003). Indigenous forests decreased by 2503ha (228 ha annual loss) while plantations declined by 2314 ha (210 ha annual loss) resulting in loss of total forest cover of 4817 ha (Table 4). 4

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Fig. 3. Converted area by cultivation in the Eastern Mau forest 1976–2014.

is Likia extension in the south western part of the complex. Likia forest block experienced encroachments by members of three communities namely; Kalenjin, Kikuyu and Maasai, through self allo­ cation (witemere-in Kikuyu dialect) (Fig. 5). Whereas the Kalenjin and Kikuyu converted the land to crop farming, the Maasai grazed through the remaining parts of the forest, and sometimes their livestock would stray into the farms destroying crops and triggering violent clashes with members of the Kikuyu and the Kalenjin communities. In the year 2010, the government moved in to contain the Likia conflicts by driving out all the people that had occupied the Likia forest extension and reclaimed the land by planting trees. Other conflicts involved the Kenya Forestry Service (KFS) and

community members. These conflicts were between forest guards and community members over illegal extraction of tree products, timber and poles. Members of the Kalenjin community were mainly involved in cutting of the tree poles and collaborated with the Kikuyu youth who transported and sold the poles. Apart from forest poaching, there were disputes over the forest cutline. Although the cutline is not the official cadastral boundary, the community preferred it when placed deep inside the forest so as to access more forest land. Often the Kenya forest service would destroy settlements inside the official boundary, sparking violent conflict with the community.

5

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Table 2 Land use change matrix for 1986 and 1995 (60 m pixel) in ha. Year

1995

1986

Land Cover Class Acacia Bare ground Built up area Grassland Shrubs Lake Cultivated fields Plantation forest Indigenous forest Total (Ha)

Acacia

Built up area 0 0 276.12 1.08 36.72 0 63 0.72 0

Grassland

Shrubs

Lake

628.56 0 0 0 2.16 0 0 0 0

Bare ground 0 272.88 0 0 0 138.24 0 0 0

0 0 0 0 0 538.92 0 0 0

Cultivated fields 0 0 0 1139.4 175.32 0 48371.4 648.36 157.68

Plantation forest 0 0 0.36 2261.16 663.12 0 199.44 18905.4 101.88

Indigenous forest 0 0 0 672.84 1069.2 0 11.52 107.64 31435.56

Total (Ha) 628.56 272.88 289.44 11718.36 8984.88 677.16 48857.76 22197.24 31757.4

0 0 0 7407.36 237.6 0 139.32 2062.08 36

0 0 12.96 236.52 6800.76 0 73.08 473.04 26.28

630.72

411.12

377.64

9882.36

7622.64

538.92

50492.16

22131.36

33296.76

125384

Plantation forest 0 0 0 474.48 270 0 253.08 6444.72 13.68

Indigenous forest 0 0 0 188.28 179.64 0 22.68 182.52 27882

Total (Ha) 630.72 411.12 377.64 9882.36 7622.64 538.92 50492.16 22131.36 33297.48

7455.96

28455.12

125384

Table 3 Land use change matrix for 1995 and 2003 (60 m pixel) in ha. Year

2003

1995

Land Cover Class Acacia Bare ground Built up area Grassland Shrubs Lake Cultivated fields Plantation forest Indigenous forest Total (Ha)

Acacia

Built up area 0 0 348.84 14.4 2.16 0 100.08 2.52 0

Grassland

Shrubs

Lake

630.72 0 0 0 9.72 0 0 0 0

Bare ground 0 323.64 0 0 0 1.8 0 0 1.8

0 0 0 4687.2 39.24 0 60.12 771.48 180.72

0 0 0 694.8 5425.92 0 16.56 1096.2 83.16

0 82.8 0 0 0 537.12 0 0 0

Cultivated fields 0 4.68 28.8 3823.2 1695.96 0 50039.64 13633.92 5136.12

640.44

327.24

468

5738.76

7316.64

619.92

74362.32

5. Discussion

logging, cultivation, forest fires, and charcoal burning hence contrib­ uting to degradation. Thus, the effect of changing multi-party politics and subsequent politics had an unintended consequence of deforestation and increased conflicts. However, after the year 2003, there was reduced rate of deforestation that could be attributed to the 2002 national elections that ushered in new leaders whose environmental policy included evicting forest settlers. However, this policy was short-lived due to the politics associated with a plebiscite to change the Kenyan constitution in 2005. The period running between 2003 and 2014 recorded an increase in violent ethnic clashes. Some of these clashes were directly linked to the forest resources competition such as land access in Likia area and pasture in Longoman forest block. In Eastern Mau, forest cover losses were associated with politically motivated legal and illegal excisions for various reasons among them settling the landless as reported by various studies (Lambrechts, 2007; Hesslerova & Pokorny, 2010; Klopp & Sang, 2011; Olang & Kundu, 2011; Langat, Maranga, Aboud, & Cheboiwo, 2016). In addition to land allocation (Table 5), in some areas, community allocated themselves land (witemere). This was facilitated by the weak forest institution as the then forest legislation Cap 385 was extremely weak on protecting the forest. The law promoted command and control with no stakeholder participation (Thenya, Wandago, Nahama, & Gachanja, 2008). For example, to excise forest land it only required the line Minister to give a 30 day notice posted on government newspaper (Kenya gazette) that had highly restricted public circulation. This allowed massive forest excision to take place incognito. Using political patronage and exploiting weak forest legislation (Cap 385), various gazette notices on forest excisions were issued (Southall, 2005). The notices indicated that the aim of degazetting the Mau forest reserve was to settle the Ogiek ethnic group who had been evicted from the forest as well as other persons who had been displaced by the 1992 tribal clashes in the Rift Valley. Some scholars (e.g Klopp, 2012; Mor­ jaria, 2012) claim that excisions were meant to settle the Kalenjin

5.1. Conflicts and forest management Kenya witnessed ethnic and political conflicts in 1992, 1997/8 and 2007/8, all of which occurred after the advent of multi-party politics, which exacerbated ethnic competition for power and natural resources. Multi-party politics were introduced in 1992 after prolonged agitation by politicians and the civil society. The introduction of multi-party politics resulted in the need to control voting patterns and the popu­ lous rift valley become the bedrock of voter manipulation and conflicts. It is notable that Kenyan politics is strongly divided along ethnic lines and parties are found to draw their support from distinct ethnic groups. Towards the end of the 1986–1995, the first major inter-ethnic clashes occurred around Lare area. The period after 1995 that indi­ cated sharp decline in forest cover, coincided with introduction of multiparty political system and planned movement of people into Mau area to create more voting blocks. At the same time there were increased in­ cidences of violence during the 1995–2003, complicating any mean­ ingful management of forest in Eastern Mau thus allowing increased expansion of settlement and clearing of forest land. The reduced ca­ pacity to manage forest resources resulted in illegal logging and con­ version of forest to farming areas. Starting the year, 1995, the Eastern Mau recorded the largest loss of forest cover far much above the national average. The decline in forest coverage could be attributed to the government initiated forest excisions, which peaked in 2001. Although the govern­ ment excisions were reported to have been stopped in the year 2001, satellite imagery analysis indicated continued forest degradation, which could be attributed to prolonged effect of inter-ethnic conflict and po­ litical patronage that contributed to illegal excisions in places such as Mariashoni and Longoman forests. Both legal and illegal excisions opened up the forest for encroachments in form of grazing, timber 6

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Fig. 4. Land cover in Eastern Mau between 1995 and 2003.

(supporters of the government) to neutralize the influence of the Kikuyu in national elections outcome as they were considered to be in the op­ position party. According to Boserup theory, some of the underlying drivers towards land degradation are weak legislation, rent seeking and political patronage, all of which characterized Eastern Mau excisions. However, regardless of their intent and purpose the excisions were preceded by more incidences of violent clashes between the Kalenjin and

Kikuyu (who had now become neighbors). There were also grievances from the Ogiek community about losing their ancestral land to other non deserving persons. In the midst of these conflicts, forest management was highly compromised and people took advantage of breakdown of law and order to open up more forest land. The excisions and forest encroachment attracted public outcry and a total of 16 objections to the excisions were received from various stakeholders including the World 7

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Table 4 Land use change matrix 2003 and 2014 (60 m pixel) in ha. Year

2014

2003

Land Cover Class Acacia Bare ground Built up area Grassland Shrubs Lake Cultivated fields Plantation forest Indigenous forest Total (Ha)

Acacia

Built up area 0 0 467.28 1.44 0 0 380.52 0 0

Grassland

Shrubs

Lake

517.68 0 0 0 39.24 0 0 0 0

Bare ground 12.24 0 0 0 24.48 0 0 0 0

77.4 321.84 0 0 200.52 619.92 0 0 0

Cultivated fields 0 5.4 0.72 1263.96 1627.56 0 72836.28 3169.08 2247.48

Plantation forest 0 0 0 328.68 117.36 0 637.2 3603.96 480.24

Indigenous forest 0 0 0 399.96 0.72 0 235.44 119.52 25202.88

Total (Ha) 640.44 327.24 468 5738.76 7316.64 619.92 74362.32 7455.96 28455.12

0 0 0 3701.52 102.6 0 272.88 510.48 464.76

33.12 0 0 43.2 5204.16 0 0 52.92 59.76

556.92

36.72

849.24

5052.24

5393.16

1219.68

81150.48

5167.44

25958.52

125384

Fig. 5. Conflict hotspot map -identified in transect walks.

Bank, IUCN-The World Conservation Union, East Africa Wildlife Society and the Kenya Forests Working Group (KFWG) (GOK, 2009). This ob­ jection by different organizations indicated that there was a lacuna in legislation on forest management. A number of forests in Kenya such as Mt Elgon, Mau Complex ecosystem and Aberdares among others were opened for settlement schemes (KFWG et al., 2005; Thenya et al., 2008). Through collusion of

government departments under political influence, large tracks of land were allocated to individuals and companies, which were used as set­ tlement for potential voters leading to forest loss and conflicts. Part of the forest management component that was infiltrated by politicians and land speculators was the plantation establishment under Shamba system. Rather than have the forester in respective forest areas operate the system, the Kenya provincial administrator under single party rule 8

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Table 5 Forest excisions and land allocation in Eastern Mau. Name of Scheme

Area [ha]

Intended Beneficiaries

Number of Intended Beneficiaries

Actual beneficiaries

Remarks

Sururu Likia Teret Sigotik

5,852 2,290 2,117 1,812

Ogiek families Ogiek families Not stated Not stated

Kalenjin majority Kalenjin majority Kalenjin majority Kalenjin majority

First Ogiek settlement in Mau in 1994 Started in 1995 Established in 1995 Started in 1994 not finalized due to disputes.

Nessuit Ngongongeri Marioshoni

4,730 4,100 8,300

Not stated Not stated Ogiek families

2,600 families 900 families 850 families 600, against 1,500 families demanding settlement 1,500 families 1,400 families 1,500 families

Ogiek majority Ogiek majority Ogiek majority

The Ogiek were already resident in Nessuit Started in 1996. Started in 1996 put to hold in 1997 through Court Injunction.

(Source: modified from GOK, 2009)

managed the system and facilitated settling of voters aligned to certain politicians in specific forest areas. Due to this kind of patronage, some local political brokers took advantage of the situation and bestowed upon themselves the role of clearing more forest outside the degazzetted portion thus leading to further degradation. As a consequence of forest degradation, inter-ethnic clashes attributable to resource use competi­ tions were witnessed in areas such as the border between Teret location (bordering Teret forest block) and Lare (bordering Longoman forest block).

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6. Conclusion This work demonstrates the significance of spatial studies in under­ standing societal challenges such as natural resources management and governance, and conflict. By using remote sensing and GIS, the study shows that conflict over natural resources can be exacerbated by politics and dynamic ethnic relationships. Underlying issues like legislations are important in explaining potential changes in land use as they have great influence on land cover changes by either mitigating or influencing land cover changes. Although it is possible to change legislation for example, in the Kenyan case, from Cap 385 to Forest Act 2005 (revised in 2016) and transformed institutions e.g from the Forest Department to the Kenya Forestry Service, there is no clear relationship between these changes and reversal in natural resource degradation. This is because the main underlying drivers of forest change in the Kenyan context include conflicts and politics which are rather fluid in nature and highly dy­ namic. Thus, conflict management and governance regimes as shaped by the political class in Kenya should be part and parcel of sustainable natural resources management in policy and practice for Kenya to realize 10% forest cover as recommended by the United Nations. Acknowledgement The authors wish to thank DANIDA for funding the research in Eastern Mau under the STAKE project and two anonymous reviewers for bringing new insights into the study. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.apgeog.2019.102115. References Alao, A. (2007). Natural resources and conflict in Africa; the tragedy of endowment. NY, USA: University of Rochester Press. Benson, B. J., & Mackenzie, M. D. (1995). Effects of sensor spatial resolution on landscape structure parameters. Landscape Ecology, 10, 13–120. Birdlife International. (2013). Important Bird areas factsheet: Mau forest complex. http: //www.birdlife.org/ Accessed 13 June 2013. Boserup, E. (2005). The conditions of agricultural growth: The economics of agrarian change under population pressure. New Brunswick, New Jersey: Aldine Transaction. Carr, D. (2004). Proximate population factors and deforestation in tropical agricultural frontiers. Popul Environ. Popul Environ, 25(6), 585–612.

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