Tanzania Mapping Land Cover Changes

Novel Forms of Livestock and Wildlife Integration Adjacent To Protected Areas in Africa – Tanzania

Mapping Land Cover Changes in Simanjiro and Monduli Districts

Mrigesh Kshatriya Shem Kifugo Fortunata Msoffe Moses Ole Neselle

Mohammed Y Said

International Livestock Research Institute 1.1 Background

The GEF Medium-Sized project “Novel Forms of Livestock and Wildlife Integration Adjacent to Protected Areas in Africa: Tanzania” aims at the conservation of globally significant biodiversity, with improved ecological integrity, conflict resolution, food security and poverty alleviation.

The objective of the GEF Medium-Sized project is to significantly reduce the conflict over access to resources through the integration of pastoralism, cropping and wildlife conservation through effective policy and institutional change. The geographic scope of the project area occurs within an ecosystem of approximately 35,000 km². The area includes two national parks (Tarangire and Lake Manyara), the Marang and Esimingor National Forest Reserves and the watershed of the Northern Highland Forest in the Ngorongoro Conservation Area. Lake Manyara is recognized internationally as a Biosphere Reserve. Tarangire and Manyara National Parks are acknowledged as keystone components of Tanzania’s tourism industry. Tarangire and Manyara are two of the highest grossing of Tanzania’s 12 national parks in terms of revenue generated and visitor numbers (the other two being Kilimanjaro and Serengeti NP’s).

The GEF Medium-Sized project will explore and understand the dynamics of land use in the selected project area and use this knowledge to improve the returns to stakeholders from both wildlife and livestock simultaneously. This will be achieved by developing and implementing land use plans and establishing benefit-sharing mechanisms from wildlife such as community-managed business ventures. Since the Village Land Act (1999) and Land Act (1999) came into force, Village Councils have been instructed to categorise their land according to pre-existing or new land use plans to be approved by the Village Assembly and subject to advice of the District Council. The GEF Medium-Sized project will support this process and will strengthen the representativeness of the Village Councils and assist communities to participate fully in planning, by providing awareness, training and relevant tools.

2 The synthesis, collection and application of new information and tools in the Tarangire-Manyara Ecosystem will help decision makers in communities, NGO’s and government to formulate improved policies to enhance peoples’ livelihoods and promote ecological integrity.

To achieve this objective, the GEF Medium-Sized project’s activities are divided into three components: ⇒ Component 1: To develop and implement participatory land use planning and Wildlife Management Areas (WMA); ⇒ Component 2: To design and implement benefit sharing mechanisms and to increase returns from integrated wildlife and livestock production systems; ⇒ Component 3: To develop Decision Support Tools in order to strengthen rational Resource-Access and Management.

1.2 lLRI’s contribution - Mapping land use changes

ILRI’s contribution was divided in two phases. The first phase was focusing on the land use change trends and the second one was to focus on the development of Decision Support Tools (DSTs).

2.1 Introduction

Over the past three decades there has been a notable change in land-uses in the Maasai-steppe including the study area, especially from subsistence to extensive agriculture. This has lead to a growing concern about the sustainability of the Maasai Steppe as an ecological system (Ecosystems Ltd., 1980; Borner, 1985b; Mwalyosi, 1991; TWCM, 2000; OIKOS, 2002b). Some of these changes have been influenced by political factors and the linkages between policies and ecological changes are poorly documented. Similar changes have also been noted in other Maasai land in Kenya (Homewood et al, 2001, Campbell et al., 2005, Norton-Griffiths et al, in press). Notable conversions to agriculture by pastoralists in the study area is linked partially to issues of land tenure insecurity, and livelihood needs, particularly, the need of the poor and the most vulnerable families (TNRF, 2005). These factors and others are likely to have negative impacts on the population of large migratory wildlife as well as the livelihoods of the local Maasai communities. This study highlights factors and processes that are believed to drive land-use change in the study area. A socio-political ecology approach is adapted focusing on the two districts of Monduli and Simanjiro due to their uniqueness and importance to the large wild herbivores (the key migratory species) and the local people. Like many of the unprotected land within the ecosystem the rangelands in the two districts is under great pressure because of the expanding cultivation and might therefore become less accessible for both livestock and wildlife in the near future, if the trend is left to continue (Voeten, 1999).

Ecologically this area is a vibrant and important stronghold for the wildlife and pastoralists of northern Tanzania (Lamprey, 1963; Lamprey, 1964). It contains the second-largest population of migratory wild ungulates in East Africa (after only the Serengeti-Mara system) as well as the largest population of elephants in northern Tanzania (Foley, 2002; Douglas-Hamilton, 1987). The Simanjiro plains in Simanjiro District is one of the most important distribution and calving areas for wildebeest and zebra (TCP, 1998; Kahurananga, 1979; 1981; 1997). Large concentrations of wildlife and domestic animals including

4 cattle, sheep and goats, donkey share pasture in this area at various times of the year, particularly the wet season (Lamprey, 1964; Kahurananga & Silkilwasha, 1997; Voeten, 1999). Factors driving large mammals migration into the Simanjiro plains during the wet seasons are linked to the availability of enriched nutrient contents of the short grasslands and soils, including Calcium and Phosphorus that are very important for lactating females (McNaughton, 1990; Galanti, 1997).

2.2 Objectives of the study

The objective of the study was to analyse the land cover and land use changes in Monduli and Simanjiro districts and characterise the drivers of these changes. In this study we conducted both broad analysis on land cover changes and also detail analysis at village level of patterns of land cover changes over two time periods of 1984 and 2000 using remotely sensed data and ground checks.

2.3 Study Area

Monduli and Simanjiro Districts are among the six districts that form the “Maasai Steppe-proper” of northern Tanzania. Others are Kiteto, Kondoa, Babati and the newly formed Longido District. The Maasai steppe is located in Northern Tanzania and falls within an eastern limb of the East African rift valley and includes Tarangire and Lake Manyara National Parks, and the surrounding dispersion areas used by migratory wildlife. The area as defined by the extent of the movements of animals is part of the Maasai-Steppe that extends from Kenya, and located between 3° 40’ and 4° 35’ South Latitude and 35° 50’ and 36° 20’ East Longitude (Lamprey, 1964). It encompasses a vast area estimated variously at between 20,000 and 35,000 sq km (Borner, 1985; Prins, 1987) and stretches from Lake Natron to the north to the Simanjiro plains and Irangi Hills-Kondoa to the south, the Ngorongoro Crater and Mbulumbulu Hills form the border on the western side (Figure 2.1).

5 The study area is situated on a tree savannah in arid country dominated by Accacia and Commiphora species (Ludwig, 2001). It arises from about 1,000m in the south-west to 2,660 m in the north-east. About 75% of the area is flatland, 22% is rolling to moderately dissected and 3% is hilly. The area is also drained by rivers which eventually empty their waters into Lakes Burunge and Manyara. Lake Burunge is mainly fed by the Tarangire River, which drains parts of Babati, Simanjiro and Kondoa districts. The river is fed by both surface run off and ground water recharge (Mwalyosi, 1999).

The movement of both livestock and wildlife is driven by rainfall and forage distribution. Also agriculture expansion is related to both temporal and spatial distribution of rainfall, soils and other factors.

Figure 2.1: The study area showing the 2 districts (Monduli and Simanjiro), the extent of the ecosystem and its proximity of protected areas.

7 The rainfall in the study area is bi-modal with short rains occurring between November to December followed by a dry spell and by a longer period of rain from March to May (Figure 2.2). The short rains are very unreliable and show a high spatial variation. The long rains are more reliable both in distribution and total amount. Rain averages to about 650 mm per annum, but can vary widely from year to year (TANAPA, 2001). As elsewhere in East Africa there exists a correlation between elevation and rainfall. Lower altitude areas tend to receive lesser than higher areas (Prins & Loth, 1988). The presence of rift valley tends to influence rainfall; areas nearer tend to show heavier and more regular rains. Mean maximum temperature is 27˚C and minimum temperature 16˚C. The extreme minimum is 4 ˚C in July and the highest maximum 40 ˚C in January. Humidity in October falls to 35 %, indicating very dry conditions (OIKOS, 2002b).

250

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0 July May April June March August January October February November December September Month

Figure 2.2: Monthly rainfall in Monduli showing rainfall pattern of the district with high rainfall between the months of November to May.

We analyzed the temporal variation in rainfall in the study area. Year rainfall anomalies were calculated for the entire time series using the z-transform ((xi

m)/std), with xi being the rainfall value for a given year i, m the mean rainfall value for that across all years, and std is the standard deviation of the rainfall value across all years. We calculated the same for wet (November – May) and dry (June – July) season rainfall. The long-term annual rainfall showed marked declines in rainfall during the period 1971-1977, 1981-1984, 1991-

8 1993 and 2002-2005, with marked increase in rainfall in the periods 1968- 1970, 1978-1980, 1985-1990 and peaking in 1962, 1968, 1979, and 1998 (Figure 2.3).

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-2.5 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 Year

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-2 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 Year

Figure 2.3: Long-term rainfall pattern of Monduli between 1961 and 2005 for the annual, wet and dry season.

9 The long-term annual rainfall in the study area showed a 4 year quasi- periodicity. Similarly, both the wet and dry season rainfall also showed similar pattern. However, the dry season showed high rainfall between 1967 and 1968, 1995 and1999 and prolonged spell of rainfall during the period 1969 and 1972, 1988 and 1994.

The flora of the study site is determined by the landscape and also the rainfall pattern in the area. The flora of the ecosystem is very rich and its subdivision into broad physiognomic types gives only some idea of the floristic diversity. More open formations (grassland-forest mosaic, grassland and wooded grassland) are often better associated with more closed formations (forest, woodland, bushland and thickets), which they may replace either in a sequence (with local changes of topographic and edaphic factors) or cycles related to the frequency and intensity of burning and cultivation (Prins & Loth, 1988). The floristic and physiognomic characteristics lead to the lacement of the area in the Acacia-Themeda species scattered tree grassland type. Commiphora, Acacia and allied genera, often of shrubby forms dominate the woody vegetation (TCP, 1998).

In the arid lowlands (1,000 m above sea level) small moist enclaves in a generally dry environment (ground water forest near Lake Manyara and some areas bordering Tarangire River) covered by extended grasslands where drainage is poor owing to volcanic ash, and by bush thickets and Acacia woodlands. Dominant grass species include Sporobolus spicatus, S. robustus, S. marginatus, Cyperus laevigatus, Themeda triandra, Panicum sp., Hyparrhenia sp., Digitaria sp. and Pennisetum sp. (Ludwig, 2001). Inside Tarangire National Park, various vegetation types are related to elevation and soil type (Lamprey, 1963). The vegetation in the park mainly consists of grasslands and open savanna woodlands. Apparent features of the park are the vast wooded grasslands which are dominated by Acacia tortilis, Balanites aegyptiaca, Adansonia digitata, Maerua triphylla and Grewia spp. and Commiphora spp. tree species on the ridges (Van de vijver, 1999). Furthermore, extensive grasslands (and flood plains) can be found in the park

10 which are dominated by grass species such as Bothriochloa spp., Cenchrus ciliaris, Dactyloctenium aegypticum, Digitaria spp., Panicum spp., Pennisetum mezianum, Sporobolus spp. and Urochloa spp. (Chuwa, 1996)

The geology is based on three types of formations: the pre-Cambrian gneiss rock and the lucustrine and alluvial deposits of Miocene origin (Van de vijver, 1999). During Miocene and Pleistocene volcanic eruptions vast areas were covered with volcanic ashes. The underlying gneiss and other pre-Cambrian crystalline rocks in much of Tarangire National Park give rise to different physical features. There are four major soil types. First there are well drained red-loams along river valleys which become increasingly more sandy and stony as slopes increases, due to lack of depth. Below 1070m, in areas of less undulation, soil types originating from lake deposits and sediments, ranging from clays to sands and are normally alkaline in nature. The fourth major type of soil is found in low-lying flood plain areas where soils are made of dark alluvial deposits that are predominantly vertisols, consisting of montmorilinitic clay components, commonly known as Black-Cotton soils. This type of soils are generally water-logged in the wet season but dry up in the in the dry season, forming cracks.

Figure 2.4: 3D map of the study area showing the terrain. The map shows the park (green) in relation to the 2 districts, the bluish tone are mountainous areas and brown to yellow toned areas are transitions areas of foot hills and plains.

12 2.4 Drivers of land-use change in Monduli and Simanjiro districts: A historical perspective

Historically, pastoralists practiced a nomadic and extensive land use system, which utilized marginal lands and water resources (Peterson, 1978; Igoe, 2000). Traditionally the Maasai lived on pastoral economy that has been evolving over time. However, recent trends of economic liberalisation (structural adjustment) have precipitated the burgeoning of business interests, including foreign business and investments. This has focused on tourist industry, large-scale farming and mining of gemstones which in reality have considerable low impacts on the local pastoralists in terms of improving their livelihood. While this trend might seem as a positive move towards a diversified economy, livestock keeping continue to play a great role in the Maasai livelihood than does mining and trade, particularly among the most poor and vulnerable people (VETAID, 1994).

The fragmentation of pastoral lands in Tanzania and particularly in the study area, are driven by both land tenure insecurity caused by pressure for expansion in wildlife conservation areas (TNRF, 2005); and livelihood interests causing people to shift from increasingly marginal pastoralism to farming for subsistence and commercial purposes. Other explanatory factors include agricultural immigrants from intensive populated areas (such as Kilimanjaro and Arusha Regions) who come to settle in the rangelands, buying portions of land and continue with their lifestyles as farmers in these areas (Goldman, 2007). In Tanzania and especially the study area, expansive agriculture in the rangelands has been associated with insularization of national parks (Borner, 1985a) due to the blockage of migratory routes from these parks to dispersal areas leading to massive extinctions of native species (Kideghesho, 2002).

The issue of land-use change in the Maasai-steppe has been mentioned over the past 40 years as a major threat on the wildlife movements outside of Tarangire National Park (Lamprey, 1964), because of its association with

13 habitat fragmentation. Today the same question is being posed with no immediate answers but still questioning the future of this important ecosystem at both regional and national level.

Results from a recent Systematic Reconnaissance Flight (SRF, 2004) by the Tanzania Wildlife Research Institute (TAWIRI) have shown that the key migratory species particularly wildebeest and zebra numbers have declined considerably over the past decade (TAWIRI, 2005). The two species are the most dependent on the Simanjiro plains during the wet season which they use as calving and grazing grounds because they contain much more nutritious grass and soils rich in minerals such as Phosphorus and Calcium essential for lactating females (McNaughton, 1990).

The history of the Maasai shows that the 19th century had a big influence in terms of changing pastoral way of life as was the colonial period that saw the displacement of the Maasai from highly potential land for agricultural development by the European farmers/settlers. This move started in northern Kenya by the British colonial and moved southern gradually to Germany Tanganyika in Tanzania. Many of the game parks were created at the same time through the eviction of people from key resources such as the dry season grazing areas and watering points. Because of the abundant water and pasture in the “proper Maasai-steppe”, it had a reputation as one of the best pastoral areas in Tanzania. Many herders who were evicted from the Serengeti National Park in the 1950s relocated in this area (Igoe, 2000).

In 1970, the Tarangire Game Reserve was upgraded to become Tarangire National Park including the southern portion of approximately one third of the area in the northern Mkungunero area. People who were residing in this area had to move to the Simanjiro plains as there were still sufficient pasture and watering points. Maasai herders living to the east of Tarangire began to feel squeezed as commercial seed bean companies and peasants from the slopes of Mountains Meru and Kilimanjaro began moving into the area in the early 1980s. By the mid 1980s the movement of commercial interests and peasant

14 farmers into the area had expanded to the villages of central Simanjiro (Igoe, 2000).

Until recently the area has been traditionally used for livestock grazing by the Maasai. During the last two decades, the land became increasingly accessible and attractive to a variety of other land users including small and large scale agriculturists, charcoal producers, resident hunters, professional sport hunting companies, photographic tour operators, and miners. An extensive degree of land cultivation has occurred on the western boundary of TNP virtually closing off all possible migratory routes (OIKOS, 2002a). To the east, habitat conversion is escalating at an alarming rate though valuable wildlife habitats and corridors remain. Essentially, without the wildlife habitats outside of the park, TNP is at risk of becoming an ecological isolate maintaining only a fraction of the magnificent wild species it once sustained in impressive abundance (Newmark, 1996; Lama, 1998). Maintaining unrestricted movement to dispersal areas outside of the national parks is therefore the key to preserving the integrity of the Maasai-steppe and the viability of Tarangire and Lake Manyara National Parks.

Land policies have partially contributed to the rapid land use changes in the study area. Agricultural conversion is being driven by both external and internal forces and influences although the relative importance of these is not well understood. The relative weakness of the livestock economy in the study area is a central part of these dynamics for both poor and rich households (TNRF, 2005). This weakness stems from loss of grazing land and water sources due to the creation of protected reserves and parks, limited access to markets and support services, drought and diseases. Simanjiro livestock economy for example, suffered a bad blow in the early 1990’s when the state withdrew provision of services such as cattle dips, which resulted in the increase of tick-borne diseases, such as the East Coast Fever. This reportedly reduced many household livestock holdings and created strong incentives for pastoralists to convert or at least to diversify into agriculture.

15 There are also antagonistic feelings at the village level towards wildlife and government conservation policies which communities view as threatening their land tenure security and livelihoods (Mosses, 2006, Pers. Comm.). A related fear of the general sense of land tenure insecurity is broader than simply loosing it for wildlife conservation. It is also connected to the new government investment policy of 2002, whereby pastoralist land could be allocated to investors (foreigners/local). This fear is a key driver towards expanding cultivation in the study area. People think that unless they can show clear ownership through farming and settling on a plot of individual land, they will eventually loose it to outside interests.

Data from the Tanzania National Bureau of Statistics (NBS) indicate a high population increase in the study area in the past 15 years (NBS, 2003). The growth is not entirely due to natural increase but to a greater extent contributed by the immigrants from nearby regions of Kilimanjaro and Arusha whose inhabitants are mainly agriculturalists. These immigrants are the ones who make deals with local Maasai in leasing out several acreages of land for cultivation in return for bags of maize (Pers. Comm., 2005 with a civil worker in the study area). These events and policy are summarised in Figure 2.5 with their time line.

Figure 2.5: Time-lines of major policies and historical events that have influenced land-use change in the study area

The expansion in agricultural areas is also linked to the fact that sometimes rainfall conditions play a great role in land-use change in this semi-arid rangelands, as most of the new farms were opened up after the El-Nino rains of 1998 (pers. Comm. with key informant, 2005). In Emboreet for example, poor households with insufficient milk and cattle are making deals with immigrants and investors e.g. farmers, where the residents lease out land in return for part of the harvests. Informal agreements where a lease of up to 50

17 Acres in return for five bags of maize equivalent to 500 Kg. in years of good rainfall (Pers. Comm. with a resident farmer, 2005). It is therefore fundamentally a food security strategy that is spreading because local people in Simanjiro lack alternatives. On the other hand while poor families require livelihood alternatives to livestock in Simanjiro, wealthier families may also do land leasing deals for farm conversion not out of economic desperation but because agriculture in years of good rainfall can be highly profitable (TNRF, 2005). Money coming from mining business at Mererani by relatives or community members has also been invested back home to open up new areas for cultivation.

3. Methodology

3.1. Data - acquiring satellite images

Fine spatial analysis of land cover was conducted using high resolution satellite images. Satellite images were acquired from United State Geological Survey (USGS). The dataset consists of 2 Landsat MSS scenes (October 1984) and two Landsat TM scenes (February 2000). These scenes covered entire Monduli district and about half of Simanjiro district, and more than 90% coverage of the ecosystem (Figure 3.1). The two date’s represent months which are not very dry or very wet. All the images were geometrically co- registered with a high accuracy of a root mean square error below the pixel size, using second-order polynomial based ground control points (GCPs). The images were projected from global coordinate system (WGS84) to local coordinate system of UTM projection, based on the 1:50,000 topographic maps of the area.

18 3.2 Mapping large and small scale agricultural fields

After georeferencing the images we masked all areas that had cloud cover in order to have the same corresponding areas for the 2 images for the year 1984 and year 2000. We used Erdas imagine to mask the cloud cover as indicated in the image in Figure 3.1.

The next step was to mosaic or combined the images together and than clip the study area that included both Monduli and Simanjiro districts. The third step was to reclassify the images using unsupervised classification. The images were classified into 100 classes based on the pixel value (Figure 3.3). In case of year 2000 image, all pixels were given land-cover classes based on Africover vegetation classes done in the same year. The Africover classes were created based on visual interpretation and so a polygon with class A will have many pixels of different classes resulting from unsupervised classification. To go round this we opted to get a minimum of 3 pixel classes within an Africover polygon with majority pixels. The 3 pixel classes were assigned land-cover classes from Africover data.

Figure 3.1: Map showing the study area and the coverage of satellites of the area, showing the path and rows of images.

Figure 3.2: Satellite images overlaid with the study area. Deep red coloration shows areas of dense vegetation and light bluish are more opened vegetation and bare ground cover.

Figure 3.3: Satellite image of the area showing areas in black as the masked areas that were covered with clouds.

Figure 3.4: A classified image with 100 classes based on the pixel value.

23 Small and homogenous polygons in colour from Africover database were considered to avoid a big variety in pixels classes within the polygon. The above procedure was repeated several times until the 100 pixel classes were assigned a Land-cover class from Africover Land-cover data. We could not exactly apply the same method to classify the 1984 mosaic, since no Land-cover data or maps were done in the same year or there about which are available to guide in Land-cover classes assignment. We therefore decided to use 2000 image characteristics to classify the 1984. Areas in both images with same characteristics were identified and pixels classes within the area in the 1984 image were assigned land-cover class to that 2000 image.

Africover polygon with land-cover class open to closed herbaceous vegetation on temporarily flooded land

Unsupervised classified Image (Pixels classified)

Figure 3.5: The maps shows area outline in blue as classified as open to closed herbaceous vegetation on temporarily flooded land, while the unsupervised classification assigns four classes bases on pixel value. A

24 combination of these pixel values was used to classify the image into broad land cover classes. The example on Figure 3.5 highlights how we classified the images. The polygon highlighted shows Africover classification of open to closed herbaceous vegetation on temporality flooded land (Figure 3.5. The unsupervised classification assigns 4 classes. The pixel classes 17,97,32,37 from unsupervised classification are within Africover class. Each of the classes above was assigned Africover land-cover class name open to closed herbaceous vegetation on temporally flooded land. The above procedure was repeated until all 100 classes were assigned land cover classes.

1984 2000 Land -cover class of image 2000 assigned to areas of similar characteristics in two images

VISUAL COMPARISON

Image 2000 classified into Africover land-cover classes.

25 Figure 3.6: Interpretation of land-cover and assigning Africover land classes land to images

Figure 3.7: Map shows the complete classification of the 2 images. These images were later classified into 10 broad classes.

Code 1- Water 2- Agriculture 3- Closed Trees 4- Open to Closed shrubs on temporarily flooded land 5- Open Shrubs 6- Open Trees 7- Closed Shrubs 8- Closed herbaceous vegetation 9- Open to closed herbaceous vegetation on temporally flooded land 10- Closed trees on temporally flooded land

Figure 3.8a: Aggrgated land –cover map for year 1984

Code 1- Water 2- Agriculture 3- Closed Trees 4- Open to Closed shrubs on temporarily flooded land 5- Open Shrubs 6- Open Tress 7- Closed Shrubs 8- Closed herbaceous vegetation

Figure 3.6b: Aggregated land cover map for year 2000

28 Form the above results, Agriculture was isolated and 3 scenarios were mapped

1. Areas where Agriculture was absent in 1984 and present in year 2000

2. . Areas where Agriculture was present in 1984 and present in year 2000

29 3. Areas where Agriculture was present in 1984 and absent in year 2000

The procedure picked area where perceived as agriculture but looked over exaggerated. For this reason fieldwork to ground truth the results was conducted.

3.3 Field verification

For every scenario above, several sample points were picked based on their proximity to roads and were given unique identifiers. The total number of points picked for sampling was 214 out of which 177 were sampled and 82 out of 177 were found to correspond with initial interpretation. The 177 sample out of 214 was because of inaccessibility to points, points being in private properties and time constraint (10 day) became insufficient to cover all 214 sample points For the purpose of planning, sample points were clipped based on villages where in each village, points with the 3 scenarios were tabulated. This was of importance for making sure at least the 3 scenarios were picked in each village.

Figure 3.7a: The 214 sample points covering Munduli and Simanjiro District were randomly selected for field work.

Figure 3.7b: Out of 214 sample point 177 points sampled that were located near roads or path were sampled and are shown on the map.

32 Table 3.1 Data Form used in the field for ground verification and gathering historical information on land use and land cover changes

Village - Emboret- SIMA 1984 2000 2006-2007 When agr Ids CHANGE X_COORD Y_COORD Agric Scale Irrigate crops Agric Scale Irrigate crops Agric Scale Irrigate Crops started Photo General comments 142 D 218922 9563147 20 A 214080 9563000 37 A 206553 9562745 141 D 215065 9560455 140 D 213809 9556149 38 A 212804 9544246

Change-code Crop A- Agriculture 1984 None Codes: types: 2000 Maize - B - No Agriculture 1984, Agriculture Scale Irrigated MZ Others Agric - 2000 Irrigated - Beans - Agric - 1 Small - 1 1 BN C - Agric in 1984 and 2000 Non Agric - Non Irrig - Ricce - D - No Agricult 1984 and in 0 Large - 2 2 RC 2000 Wheat - Wh

The coordinates and ids were uploaded into a Global Positioning System (GPS) was used to locate the Sample points. The following attribute value were picked upon locating the point: 1) Was there Agriculture in 1984, 2000 and 2007, 2) If small or Large scale, 3) Irrigated or not 4) When did Agriculture start, 5) Photographs were taken 6) Crop types 7) The change type picked during initial interpretation in the office was tested (A, B, C and D), 8) General comments. We did this with local Maasai elders who have the history of the area and also to help us interview locals a bout changes in Agriculture.

Figure 3.8: Field verification of land-cover and land use changes in the Study area. The maps show Point 78 corresponds to the photograph- No Agriculture 1984, Started Late 1990’s Non Irrigated, Small Scale, and Abandoned 2005.

After the field work new maps were generated. In order to evaluate and compare the classification quality a random-sampling accuracy assessment was conducted, comparing the classification results to a randomly chosen set of reference points. The following statistics were computed for each

34 classification result, error matrix, overall accuracy, producer’s accuracy, user’s accuracy and Kappa statistics. The overall accuracy of agriculture fields was about 99% and Kappa statistics was 0.98 (refer to Table 3.2).

Table 3.2a Classification accuracy assessment report - Error Matrix Classified Data Unclassified Class 1 Row Total Unclassified 302 0 302 0 30 30 302 30 332

Table 3.2b Classification accuracy assessment report - Accuracy totals Class Name Reference Classified Number Producer Users Totals Totals Correct Accuracy Accuracy Unclassified 302 303 302 Class 1 30 30 30 100% 100% 332 333 332

Overall Classification Accuracy = 99.7%

Kappa Statistics

Overall Kappa Statistics = 0.9820

3.2 Mapping land cover conversions at fine spatial scale

We analysed the difference in agriculture between years in the study area using the Wilcoxon signed rank test. Wilcoxon signed rank test is a non- parametric method which is used as an alternative to the two-sample Student's t-test. Usually this test is used to compare medians of non-normal

35 distributions. We further analysed the difference in agriculture in the six villages and between the two periods through Kruskal-Wallis test.

3.4 Relation between agriculture expansion and rainfall

Analysis of agricultural expansion within the study area was conducted in relation to rainfall. Norton-Griffiths et al. (in press) found strong land cover changes in areas of high rainfall in the Mara ecosystem, which is also amongst other rangeland districts in Kenya. The rainfall data was extracted from ACTS database (Mudsprings Inc. 1999) and clipped for the study area. The rainfall was classified into 5 classes of less equal to 530mm, greater than 530mm but less or than 590mm, greater or equal to 590mm but less than 650mm, greater or equal to 650mm but less than 750mm, greater than 750mm. This layer was overlaid spatially to both agriculture of 1984 and 2000 extracted from satellite imageries. Areas of agriculture were summarized based on rainfall bands using histograms.

3.5 Relation between agriculture and distance to park and villages

Next we analysed the expansion of agriculture in relation to distance from park and villages. Our hypothesis is that as we move away from the park we would expect an increase in agriculture expansion. While, expansion of agriculture from villages the reverse might to true in that we expected agriculture to decrease as we move away from villages if proper land use zonation was observed. The six villages selected by the project are Lolkisale, Lobosiret, Lobosoit, Narakauo, Naitolya and Mswakini-Juu. The analysis was conducted in GIS and a distance map for all six villages was constructed and overlaid with agriculture map. The resultant table showing the frequency and distance was analysed. The percentage area under agriculture within each distance was calculated plotted against the distance.

3.6 Mapping wildlife and livestock movements in relation to land cover

The impacts of agriculture on wet season range and migratory corridors was analysed by spatial overlaying the maps of wildlife movements (data assembled from the Lamprey study of 1963; refer to Lamprey 1963), wet season range of key wildlife species based on the distribution of these species composed from various aerial and ground census. Maps showing the extent of the overlap between wildlife and agriculture were developed and analysed to come up with a matrix of which species might likely be displaced by agricultural development.

37 4.0 Results and discussion

4.1 Broad trends land cover changes - Agriculture changes

Agriculture has increased five folds in the study area from an area of 170 km2 (17,000 hectares) in 1984 to about 881 km2 (88,100 hectares) in 2000. Almost 3% of the total land is under agriculture 2000, while in 1984 less than 0.6% of land was dedicated to agriculture (Figure 4.1a). The increase in agriculture in the study areas was statistically significant (Wilcoxon rank test, Z = -142.4211, P<0.0001). This was also exhibited at the district level, where both Monduli (Wilcoxon rank test, Z = 94=3.7202, P<0.0001) and Simanjiro (Wilcoxon rank test, Z = 93.5641, P<0.0001) showed significant increases in agriculture area (refer to Table 4.1, 4.2 and 4.3). There was significant difference in mean field size in the two districts in 1984 (Wilcoxon rank test, Z = 7.3334, P > 0.0001). In contrast the mean field size of study area in year 2000 was about 0.0196 km2 but varied significantly between the two districts (Wilcoxon rank test, Z = 32.1576, P > 0.0001). The mean field size in Monduli is about 0.0142 km2 and in Simanjiro about 0.0424 km2. However, we also noted that agriculture fields in Monduli expanded by more than 7.5 times compared to agricultural expansion in Simanjiro district that increased by 3.5 times (Figure 4.1b and 4.1c). The land-cover changes indicate about 75% areas under agriculture in 1984 was abandoned and was not under cultivation in 2000. Most of the abandonment occurred in Simanjiro (79%) compared to Monduli (48%). Simanjiro is more arid and the soils are mostly sandy soils and this could relate to the high rate of abandonment of farms in the area.

In terms of spatial extent of agriculture in 1984 few of the villages had extensive agriculture in the two districts, and this expanded rapidly in the year 2000 (Table 4.4). We see rapid or significant expansion of agricultural fields in all the six of villages chosen for this study. And these differences are significant across the six villages and between the years (Kruskal-Wallis test, χ = 490, df = 5, p < 0.0001).

Ngorongoro Kilimanjaro Ngorongoro CA C A Kili manjaro NP NP Mount Mt Meru Meru NP NP Manyara Manyara NP NP

Lolkisale Lolkisale GCA GCA

Tarangire Tarangi re NP NP

Mkungunere Mkungunere G C Area Gam e Reserve Not covered Not Covered

A B

Ngorongoro Ngorongoro Kilimanjaro CA CA NP Kilimanjaro NP Mount Meru Mt Meru NP NP Manyara Manyara NP NP

Lolkisale Lolkisale GCA GCA

Tarangire Tarangire NP NP

Mkungunere Mkungunere G C Area G C Area NOT COVERED NOT COVERED

C D

60 0 60 120 Kilometers

Figure 4.1a: Map of the land cover changes in the study area, (a) agriculture distribution in the study area in the year 1984, (b) agriculture distribution in the study area in the year 2000, (c) areas where agriculture was abandoned by year 2000 but was present in 1984, (d) areas where agriculture was present in years 1984 and 2000

#Matale #Matale

Namanga# Namanga#

#Longido #Longido

Engut oto Engut oto # #

Esilalei Esilalei # #

Makuyuni# Makuyuni#

#MtowaMbu #MtowaMbu

#Lolkisale #Lolk isale

#Ernboreet #Ernboreet

A B

# Matale # Matale

Namanga# Namanga#

#Longido #Longido

Engut oto # Engut oto #

Esilalei # Esilalei # Makuyuni# Makuyuni# #MtowaMbu #MtowaMbu #Lolkisale #Lolk isale

#Ernboreet #Ernboreet

D C N 0100Kilometers

Figure 4.1b: Map of the land cover changes in Monduli, (a) Agriculture distribution in the Monduli district in the year 1984, (b) Agriculture distribution in the Monduli district in the year 2000, (c) Compared to A it is areas where agriculture was abandoned by year 2000 but was present in 1984, (d) Agriculture was present in years 1984 and 2000

40 Table 4.1: Land cover changes in the study area

1984 2000 (n = 16,115) (n = 45,049) Sum 169.5827 881.2071 Mean 0.0105 0.0196 Median 0.0006 0.0024 Std Dev 0.3928 0.2486

Table 4.2: Land cover changes in Monduli 1984 2000 (n = 6,774) (n = 36,531) Sum 70.0719 519.6737 Mean 0.0103 0.0142 Median 0.0006 0.0026 Std Dev 0.1147 0.1671

Table 4.3: Land cover changes in Simanjiro 1984 2000 (n = 9,341) (n = 8,518 ) Sum 99.5108 361.5334 Mean 0.0107 0.0424 Median 0.0066 0.0049 Std Dev 0.5064 0.4544

41 # # Msitu wa Tembo Msitu wa Tembo #Lolkisale Lolkisale#

#Terat Terat# #Ernboreet Ernboreet#

Loibor-Siret # # Loibor-Siret

#0rk esum et #0rk esum et NOT COVERED NOT COVERED

A B

# # Msitu wa Tembo Msitu wa Tembo Lolkisale# Lolkisale# # #Terat Terat Ernboreet# #Ernboreet

# Loibor-Siret# Loibor-Siret

0rk esum et #0rk esum et # NOT COVERED NOT COVERED N

C D 100 0 100 Kilometers

Figure 4.1c: Map of the land cover changes in Simanjiro (a) agriculture distribution in the Simanjiro district in the year 1984 (b) agriculture distribution in the Simanjiro district in the year 2000 (c) areas where agriculture was abandoned by year 2000 but was present in 1984 (d) areas where agriculture areas that were present in years 1984 and 2000

Table 4.4: Land-cover dynamics of agriculture in the study area Agriculture Agriculture Change in No Change Abandoned area (km2) area (km2) area Area (Km2) Area (Km2) in 1984 in 2000 (Km2)

Study Area 169.6 881.0 711.4 41.7 (25%) 127.8 (75%)

Monduli 70.1 519.6 449.6 21.3 (30%) 48.8 (70%)

Simanjiro 99.5 361.5 262.0 20.5 (21%) 79.1 (79%)

4.2 Map of land-cover changes in relation to rainfall

At the district level the relation between agriculture and rainfall shows a variation in pattern. The expansion partly follows the rainfall zones but also could be based on the long-term rainfall trend (refer to Figure 2.3). Agriculture increased significantly in all rainfall bands. In the lowest rainfall band (<500mm) it increased by about 3%, the medium rainfall bands (530-750mm) it increase by about 15%, but in the highest rainfall band (>750mm) agriculture increased by more than 30% (Figure 4.2). Also, as indicated in the rainfall analysis the period 1984 was very dry compared to the period 2000. Preceeding the period 2000 there was moderate rainfall in the study area. The proceeding years show low rainfall in the study area and this could impact on the expansion of agriculture in the area or it could have made people to try and cultivate in many places to reduce the risks of crop failure. Also it should be noted that the El-Nino of 1997/98 had a significant impact in the area in terms of expanding cultivation, even local pastoralists started opening new fields and leasing out to immigrants land (interviews of local people during the field surveys).

43 10

r 8

g 6

u 4

% 2

Agriculture 2000 0 Agriculture 1984 0 0 0 0 0 53 59 65 75 75 0- 0- 0- > 53 59 65 Rainfall Band Figure 4.2: Agriculture changes in Monduli and Simanjiro between 1984 and 2000.

Figure 4.3 shows the spatial pattern of agriculture expansion and indicates a south-east north-west expansion along a rainfall gradient. The concentration of land cover changes occurred at central portion of the study area, areas of high rainfall. New areas of agriculture were developed year 2000 in the north- east of the study areas in areas of medium rainfall (530-590mm) and are expanding southwards.

Figure 4.3: Map showing distribution of agriculture in relation to rainfall for the period 19884 and 2000.

45 At district levels we observe similar pattern in expansion of agriculture in relation to rainfall. In Monduli we observe high increases in agriculture in rainfall bands >750mm (9%) and 530-590mm (4.6%) and moderate increases in 650-750mm (2%) rainfall band, while in Simanjiro we observe high increase in lower bands of less than 650 mm and moderate increases in high rainfall band (refer to Figure 4.4a and 4.4b).

e 10

i 8

e 6

a 4

% 2 Agriculture 2000 0 Agriculture 1984 0 0 0 0 0 53 59 65 75 75 0- 0- 0- > 53 59 65 Rainfall Band

Figure 4.4a: Area under agriculture in different rainfall bands in Monduli district.

r 4

g 3

u 2

% 1

Agriculture 2000 0 Agriculture 1984 0 0 0 0 0 53 59 65 75 75 0- 0- 0- > 53 59 65 Rainfall Band

Figure 4.4b: Area under agriculture in different rainfall bands in Simanjiro district.

Figure 4.5a: Map showing distribution of agriculture in relation to rainfall for the period 1984 and 2000 in Monduli district

Figure 4.5b: Map showing distribution of agriculture in relation to rainfall for the period 19884 and 2000 in Simanjiro district.

48 4.3 Relation between Protected areas and agriculture fields

Agriculture in the early 1980’s was limited in its distribution and were mainly located north-west of Tarangire, mainly in Lolkisale and western section of Tarangire ecosystem (Figure 4.6), north-east of Loibor-Siret. Few fields were observed in Lolkisale Game Control Area (GCA), with some few scattered fields north of the park and mainly located at Engutoto and Esilalei. This changed drastically in the year 2000 with extensive expansion of agriculture occurring in the eastern sections of the study area and intensive concentration of farms in north-east of the study area, between Ernborret and Lolkisale, Makuyunni, Esilalei and Engutoto, areas around Namanga (Figure 4.6). In Simanjiro the extent was from Terat, Loibor-Siret up to Orkesumet south of the study area.

Figure 4.6: Map showing agriculture in relation to protected areas in a) 1984 and b) 2000.

49 The relation between agriculture and distance from protected areas shows in 1984 we had 4 to 5 major centres of agriculture (Figure 4.7) but that were not wide spread. The pattern in 2000 change and we at least 3 centres of agriculture activities and is very wide spread (Figure 4.7), with increase agriculture in some of the protected areas, especially in Lolkisale GCA.

) 1500 ) 1500

(

1000 1000

o 500

F 0 F 0 -30 15 60 105 150 -30 15 60 105 150 Distance from Protected Area (Km) Distance from Protected area (km)

Figure 4.7: Histogram showing the relation between agriculture fields in relation to distance from protected area in 1984 (left) and 2000 (right) figure.

4.4 Land-cover patterns at village level

As most of the agriculture expansion emanates from villages, we investigated land cover changes in 6 villages that were mainly within the boundary of the ecosystem. The six villages chosen were Lobosoit, Mswakini-Juu, Lolkisale, Lobosiret, Naitolya and Narakauo. Spatial land cover analyses indicate Lobosoit (increase of 39%) and Mswakini-Juu (80%) had moderate increases in agriculture as indicated in Table 4. Lolkisale had about two and half fold increase in agriculture, while Lobosiret had a significant increase of about five fold. The two villages that had very significant increase in agriculture were Naitolya and Narakauo. Naitolya had an increase of about 20 times and Narakauo about 40 fold. Both villages had little agriculture in 1984. The village

50 with largest area under agriculture is Lolkisale having about 68 km2 or 6,800 hectares and one with the least agriculture is Lobosoit (5.7km2 or 570 hectares).

Table 4.5: Agriculture changes at village level for period 1984 and 2000 Village Area Area % Z-value P-Value (km2) (km2) Change 1984 2000 Lobosoit 4.1 5.7 39 21.6193 <0.0001 Mswakini-Juu 5.4 9.7 80 -16.2829 <0.0001 Lolkisale 28.9 67.7 134 -54.8781 <0.0001 Lobosiret 3.3 18.8 470 -26.5764 <0.0001 Naitolya 1.1 24.9 2163 -17.5154 <0.0001 Narakauo 0.5 21.1 4120 -17.4636 <0.0001

Figure 4.8: Agriculture in relation to the six villages

51 However, as indicated in Figure 4.8, the forces or pressure to cultivate seems to occur from outside the ecosystem with tremendous expansion in year 2000 of these areas. We further investigated the pattern of agriculture distribution in the six villages. Agriculture in Lobosiret in 1984 was mainly near around village in south western section (Figure 4.10a) and was within 6km from the centre of the village. This expanded widely in year 2000 and agriculture occurred in all direction with almost four centres of expansion (Figure 4.9), and extended almost 15km for centre of the village. Most of the areas under agriculture in 1984 were abandoned in 2000. In Lobosoit most of the agriculture activities were consolidated in four areas, south of village, north and north east (Figure 4.10b). However, in 2000 we observed more fragmented and dispersed fields, with some of the fields exceeding 10 km from the village centre. Mswakini-Juu and Naitolya show similar pattern in that in 1984 there were few areas or land under agriculture, but in 2000 these are highly spread with certain areas have high areas under agriculture (Figures 4.10c and 4.10d). This is highlighted in graphs distance from villages were within 5 km but the intensity and spread increased in year 2000 (Figure 4.9). The pattern in Narakauo shows an increase in spread but more localised (Figure 4.10e). We have two major centres of agriculture which are located further from the centre of the village about 5km (Figure .49). Recent development shows that the farms are getting near to village centre and two centres north-west and on eastern section of the village are increasingly being cultivated.

The implication of these changes mostly depends on the direction of movement of new agricultural fields and if there coincide with the migratory corridors or wet season range for wildlife. Only one of the villages show concentration of agriculture in certain landscape and not wide spread and other 5 villages show outward expansion of agriculture. The next section of the report investigates the likely impacts of change in land use to conservation and livestock mobility and distribution.

52 Lobosiret Lobosiret

20 20

) )

4 0

8 0

9 15 0 15

1 2

( (

a a

e e

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a a

e 10 e 10

r r

u u

t t

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c c

i i

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A 5 A 5

% %

0 0 0 5000 10000 15000 0 5000 10000 15000 Distance from Village (m) Distance from Village (m)

Lobosoit Lobosoit

20 20

)

0 15

9 15

(

e 10

10 r

A A 5

0 0 0 5000 10000 15000 0 5000 10000 15000 Distance from Village (m) Distance from Village (m)

Lobosiret Lobosiret

20 20

) )

4 4

8 8

9 15 9 15

1 1

( (

a a

e e

r r

a a

e 10 e 10

r r

u u

t t

l l

u u

c c

i i

r r

g g

A 5 A 5

% %

0 0 0 5000 10000 15000 0 5000 10000 15000 Distance from Village (m) Distance from Village (m)

Figure 4.9a: Histogram showing the relation between agriculture area in relation to distance from villages in 1984 (left) and 2000 (right)

53 Mswakini Juu Mswakini Juu

40 40

) )

4 0

8 0

9 30 0 30

1 2

( (

a a

e e

r r

a a

e 20 e 20

r r

u u

t t

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A 10 A 10

% %

0 0 0 5000 10000 15000 0 5000 10000 15000 Distance from village (m) Distance from Village (m)

Naitolya Naitolya

40 40

)

0 30

9 30

(

e e 20

20 r

A A 10 10

0 0 0 5000 10000 15000 0 5000 10000 15000 Distance from Village (m) Distance from Village (m)

Narakauo Narakauo

20 20

)

0 15

9 15

(

e e 10

10 r

A A 5 5

0 0 0 5000 10000 15000 20000 25000 0 5000 10000 15000 20000 25000 Distance from Village (m) Distance from Village (m)

Figure 4.9b: Histogram showing the relation between agriculture area in relation to distance from villages in 1984 (left) and 2000 (right)

Figure 4.10a: Map showing the distribution of agriculture field in relation to the distance from the main village of Lobosiret.

Figure 4.10b: Map showing the distribution of agriculture field in relation to the distance from the main village of Lobosoit.

Figure 4.10c: Map showing the distribution of agriculture field in relation to the distance from the main village of Lolkisale.

Figure 4.10c: Map showing the distribution of agriculture field in relation to the distance from the main village of Mswakini Juu.

Figure 4.10d: Map showing the distribution of agriculture field in relation to the distance from the main village of Naitolya

Figure 4.10e: Map showing the distribution of agriculture field in relation to the distance from the main village of Narakauo

60 5.0 Implications of change on wildlife and livestock distribution and movements

One of the major impacts of land use changes from rangelands to agriculture is not only blockage of migratory routes for wildlife, but also loss of land and passage for livestock. In the study area a number of wildlife migratory corridors have been lost in the last 40 years. The western corridors are already blocked (reference). Further, we have observed that the range and densities of the migratory animals gave declined (refer to Figures 4.11a, 4.11b and 4.11c). Long-term studies in other regions of East Africa that had large migratory population such as the Mara and Athi-Kaputiei ecosystem in Kenya have shown that agriculture expansion, loss of wet season range and intensity of settlement and urban development has an impact on the population of the wildebeest and ungulates (refer to Ottichilo et al., 2001, Serneels and Lambin 2001, Serneels et al., 2001, Reid et al., 2007, Norton-Griffiths et al., in press). Recent studies by Ottichilo et al, (2001), Serneels and Lambin (2001) and Homewood et al., (2001) report large declines in resident wildebeest population in the Mara ecosystem. The resident population of wildbeeste declined from 150,000 to a population of about 40,000 animals. Most of the decline was due to loss of wet season range through large scale wheat farming (Ottichilo et al. 2001 and Serneels and Lambin, 2001; Broten and Said, 1995, Norton-Griffiths in press). In contrast in Athi-Kaputiei ecosystem we observed drastic declines in wildebeest population due to land fragmentation through land subdivisions and fencing of land (Reid et al., 2007, Said et al., in prep). In Athi-Kaputiei many of the corridors have been blocked by urban development and observed a drastic decline in the wildebeest population (Reid et al, 2007). The same process of land transformation that was being observed in Kenya in the last 30 years is being replicated in the study area.

The expansion in agricultural in Monduli and Simanjiro areas as depicted from the remote sensing data of satellite images of 1984 and 2000 indicate large increases in agriculture fields. Linked to this is the fact that sometimes rainfall

61 conditions play a great role in land-use change in this semi-arid rangelands, as most of the new farms were opened up after the El-Nino rains of 1998 (pers. Comm. with key informant, 2005). In Emboreet for example, poor households with insufficient milk and cattle are making deals with immigrants and investors e.g. farmers, where the residents lease out land in return for part of the harvests. Informal agreements where a lease of up to 50 Acres in return for five bags of maize equivalent to 500 Kg. in years of good rainfall (Pers. Comm. with a resident farmer, 2005). It is therefore fundamentally a food security strategy that is spreading because local people in Simanjiro lack alternatives. On the other hand while poor families require livelihood alternatives to livestock in Simanjiro, wealthier families may also do land leasing deals for farm conversion not out of economic desperation but because agriculture in years of good rainfall can be highly profitable (TNRF, 2005). Money coming from mining business at Mererani by relatives or community members has also been invested back home to open up new areas for cultivation.

Campbell et al., (2000) observed that large areas of pastoral lands in southern Kenya are now becoming fragmented with a large portion being converted into agriculture land, and thus increasing the exclusion of pastoralists and wildlife from the highest potential land. Serneels and Lambin (2001) and Homewood et al., (2001) concluded that land use policy is a major factor influencing the conversion of rangelands to cultivation in pastoral areas of East Africa. Policy instruments in particular affect the decision-making process of agro pastoralists and therefore modify land use changes and their impacts on the ecosystem (Homewood et al., 2001; Homewood, 2004).

Current anecdotal links the declines in the population of the large mammals within the study area, like wildebeest and zebra to changes in land-use particularly the conversions of rangelands to agriculture in the Simanjiro plains and the over exploitation of the wildlife species from both legal and illegal hunting (TNRF, 2005). The expansion of agricultural areas in the Simanjiro and Monduli districts has grown from the early 1980s but peaked after the El-

62 Nino rains in 1997-1998. Although there have been many times when people did not harvest anything due to erratic and unpredictable rainfalls, majority of the local people continue giving out more land for cultivation in form of a win- win-loose situation. It is believed that in years of good rains like in 1998 families that cultivated more land became far better off in terms of food security and also re-stocking their herds after the periodic droughts (e.g. 1993-95) by selling maize to other pastoralists in neighbouring areas. In the study area, speculations on fears related to expansions of wildlife protected areas such as parks is one of the reasons why local people are leasing out land to immigrants and investors for extensive farming particularly in the Simanjiro plains (TNRF, 2005).

Government policies related to land tenure and use have played a key role in shaping land use trends since the colonial era; including the exclusion of people from gazetted wildlife reserves and parks. In a way these policies have continued to marginalize pastoralists through the eviction from key potential areas for livestock grazing in the name of conservation. While wildlife conservation could be a competing land use form in these rangelands there is lack of involvement of local people in the formulation of plans and policies (Goldman, 2007) as well as well as lack of empowerment and support in recognizing potentials for wildlife-related tourism economy. Government support to agricultural policies failed to recognize pastoralism as a key part of it by supporting the conversions of rangelands through large scale farming leading to loss of the free ranging land where pastoralists and their livestock have co-existed with wildlife for centuries.

In the northern Maasai pastoral areas of northern Kenya there is a drive to keep the land open through land leases, conservancies, or through payment of ecosystem services (Reid et al., 2007, Kristjanson et al., 2002, Nkedianye 2004). This is in realisation that the communities need to come up with viable land uses that are compatible with the pastoral livelihoods like keeping of livestock. Secondly, diversify their incomes through conservation and eco- tourism schemes to supplement their income. In many of these areas income

63 from conservation double the people incomes during droughts and cushion them from adversity of such calamities (Kristjanson et al., 2002; reference from Northern Kenya). These schemes are also being practised in Northern Kenya in pastoral societies of Laikipia and Samburu. In the study area we foresee equal opportunities gaining incomes from wildlife and some of the villages are already reaping the benefits. But as shown in the analysis the community need to come up with a more integrated land use plans. Our analysis reveals that expansion of agriculture is very random and more and more wildlife and livestock rearing areas are being converted to agriculture. We need to further explore explicit spatial models to determine what are the factors that determine the expansion of these agriculture areas as to come up what are the possible areas that might be effected and what impacts can it have on wildlife conservation and also the pastoral livestock keeping.

64 #Matale #Matale

#Namanga #Namanga

# Longido # Longido

# #### # ## # # Engutoto # # ## # # # # # # # ## # # ## ## # Engutoto # # # # # ## Esilalei # # # ## # # ## # ### # # ## ## #### # # # Esilalei Makuyuni ### # # ## # # # # # # #Makuyuni# # # # Mtowa# # # # # #### # # # ## # Mbu# ### # # ## Mtowa# # ## # # # # Lolkisale # # # # Msitu wa # ### ########## Mbu## # ## # Lolkisale# # ## ### # ## ### # # # Tembo # #### ### # # Msitu wa # ## ###### # ### ### # #Terat # ### # #### # # # # #### #### # # # # Tembo # # ### ## ## # # ### # ### # # # #### # ## # # # # #Terat # # #### ### # # ## # # Ernboreet # # # # ## ## # Magugu ## # # # # #### ## # ### # ## ## # ### # # ## # # ### # ## ## # Ernboreet Magugu# # ### # ## # ## # ### #### ### # # # # ## ## # ## # # # # # ### # # # ## ### # # # # # # # # # # # ### ### # # # # # # # # # # # # Loibor-Siret # # # # # # Loibor-Siret # # # # # # # #

# # # # 0rkesumet# # # # 0rkesumet Low # # # # Medium # Low # Medium # High # High # # Migratory routes N N Migratory routes Tarangire Ecosystem Agriculture 1984 40 0 40 80 Kilometers Agriculture 2000 40 0 40 80 Kilometers Protected Areas Protected Areas

Figure 4.11a: The map on the left shows wildlife migratory species dry season distribution (based on data collected between 1984 and 1994) in relation to agriculture in 1984 and the right map is wildlife migratory species distribution (data collected 1997 – 2004) in relation to agriculture gathered from 2000 survey

65 #Matale #Matale

#Namanga #Namanga

# # Longido Longido

# ##### # ## ## #### ## ## ### ## # ## ## # # Engutoto #### Engutoto ##### # # ### # # ## # ## # Esilalei## # ## ## ### ## ## Esilalei ## # ## ### # # # # ## # ## # ## # ## ## Makuyuni # # ## ## # # ##Makuyuni# # # # # ##### # # ### # # # ## # ## # # # # # ## Mtowa# ## Mtowa# # # # ## # # # Mbu## ## # Mbu## # # # Lolkisale # Lolkisale # Msitu wa # # Msitu wa ### ## # # # # # # ## ### Tembo # # # Tembo ## ## #### ## ## #Terat # # # # #Terat # ## ### ### ###### # # # # # # ## # ## ## ### ## ### # ## # ## # # # # # ## # ## ## ## #####Ernboreet## ###### ## # # # ###Ernboreet### ## # Magugu Magugu# # # # ## ## ## ## ## ## # # ## # ### # ### # # # ## # # ## ## ### # # ## # # ## ### ## # # ## ## ## ### # ### # # # # # # ## ## # # # # # # # ### ### # ## # # ## # # # # ## ## # # # # # # # ## # # # # ## ## Loibor-Siret # # # # # Loibor-Siret ## ## # # # ## ## # # # # # ## ## ## # # #### # ## ### ## ## #### # ## ### # # ## # # # ## ## ## # #### # # # # # ## # # 0rkesumet ## # # 0rkesumet# # Low # # # Low # Medium # Medium # High # High

# # N N Migratory routes Migratory routes

Agriculture 1984 40 0 40 80 Kilometers Agriculture 2000 4004080Kilometers Protected Areas Protected Areas

Figure 4.11b: The map on the left shows wildlife migratory species wet season distribution (based on data collected between 1984 and 1994) in relation to agriculture in 1984 and the right map is wildlife migratory species distribution (data collected 1997 – 2004) in relation to agriculture gathered from 2000 survey.

Figure 4.11c: Map showing agriculture in relation to migratory routes for the year 1984 (left) and 2000 (right), a number of migratory corridors have been blocked through land use changes.. Migratory routes were mapped by Lamprey in early 1960.

67 References

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