Planning Assessment
Urban Expansion and its Effect on the Tenure Security of Peri-Urban Landholders in Ethiopia
The Case of Robe Town
2025 Volume 13 Issue 2 Pages 261-287
Details
2025 Volume 13 Issue 2 Pages 261-287
This study aims to assess the degree, rate, and direction of Robe Town's spatial expansion to the surrounding rural kebeles (the lowest level of administrative structure in Ethiopia) and its consequent influence on the tenure security of the peri-urban landholders. 202 sampled household heads provided socioeconomic data, while Landsat images from 1987, 2002, and 2020 provided spatial data. The findings revealed that between 1984 and 1994, 1994 and 2007, and 2007 and 2020, the town grew annually by 18.6%, 11.3%, and 9.3%, respectively. The area increased from 5.1 km2 in 1984 to 80.2 km2 in 2020, and the entire area increased by 40.9%. Over the past 33 years, the built-up area has expanded most rapidly in a southerly direction, at a distance of 9.91 kilometers from the town's mean center. Subsequently, the built-up area grew from 4.38 km2 in 1987 to 27.9 km2 in 2020. Therefore, in 33 years, around 10.2 km2 of farmland had become a built-up area (1987–2020). Such dynamicity of land use conversion consumes the peri-urban agricultural lands. At the peri-urban areas of Robe town 48.9% of the land owners lost around half of their farmland. In turn, this makes peri-urban landowners feel insecure about their tenure due to fear of eviction and expropriation at minimal cost. Therefore, in order to have excess land in the urban land bank and to meet the rising demand for housing, the town administration needs to incorporate peri-urban open spaces into urban jurisdiction with fair compensation.
Worldwide population expansion is on-going, which has led to a rapid urbanization rate. By 2050, 2.5 billion more people will live in cities around the world because of this trend, increasing the level of urbanization to 66% (UN-HABITAT, 2018). According to projections, the proportion of Africans living in urban areas will increase from 11.3% in 2010 to 20.2% by 2050. In 2020, Sub-Saharan Africa had an annual urban population growth rate of 3.98%, which is rapid compared to a global rate of 2% (Koroso, Monica et al., 2021). Similarly, Ethiopia's urban population is expected to triple with an annual growth rate of 3.8%, reaching 42.3 million people by 2037 (CSA, 2014) and 74.5 million people by 2050 (DESA, 2018). According to the report of the, the (Bank, 2017) largest portion of Ethiopia’s urbanization has been taking place in intermediaries and small towns. The trend is expected to continue for the coming two decades and result in horizontal urban expansion toward peri-urban areas (Koroso, Monica et al., 2021; Wubante, Steven et al., 2019).
According to the report of the [United Nations Economic and Social Council (ECOSOC, 2019)] globally, urban area expansion is surpassing population increase. Between 2000 and 2014, urban boundaries grew on average 1.3 times more quickly than their population. Cities in developing countries are growing much more quickly. For instance, Sub-Saharan Africa's built-up area rose by an average of 4.8% between 2000 and 2015. Over this period, growth in the region's smaller cities averaged 5.4% yearly (Forget, Shimoni et al., 2021). Likewise, the regional Ethiopian cities of Bahir Dar and Hawassa increased in size by 9% and 6%, respectively (Gashu, Kassahun and Gebre-Egziabher, 2018). Mekelle grew by 8% annually (Fenta, Yasuda et al., 2017), while Adama City expanded by 14.6% annually (Manikandan, 2019). Such intermediary cities are expanding more quickly than the principal metropolis, Addis Ababa Organization for Economic Cooperation and Development Private Sector Investment (OECD/PSI, 2020). Because of changes in land usage brought on by urban expansion, there has been a change in land ownership that results in tenure insecurity if not well managed (Adam, 2014a, 2014b; Agegnehu and Mansberger, 2020; Mbiba and Huchzermeyer, 2016; Muhabaw, 2019; Teklemariam and Cochrane, 2021; UN-HABITAT, 2018; Wubneh, 2018)
Expansion of the built environment to the peri-urban area has been considered both an opportunity and a threat by the peri-urban landholders. In western countries like Italy, where land is private property, the expectations and interests of the peri-urban communities are often high on the territorial expansion of cities to the peri-urban area, as it highly benefits them and sustain urban development through fostering productive economic activities (Wandl and Magoni, 2016). However, in the situation of developing countries, the local communities consider the horizontal expansion of cities a threat as it affects them variously (Wandl and Magoni, 2016; Wubie, de Vries et al., 2020). In Chinese cities, urban sprawl to the peripheries is considered a challenge as it creates social instability due to deferment in compensation payments and production decreases in the interval between land acquisition and development (Fang, 2016). Likewise, (Wehrmann, 2008) argued that the expansion of African cities to the peri-urban zone creates tenure insecurity that drives them to the informal land market. In Ethiopia, several researchers (Adam, 2014b; Admasu, 2015; Agegnehu and Mansberger, 2020; Teklemariam and Cochrane, 2021; Wubneh, 2018) reported both the formal and informal expansion of cities as a threat to peri-urban landholders as it creates tenure insecurity and land use instability.
Review of literatures on urban land use dynamicity and tenure insecurityThe majority of peri-urban land is rural and used for agriculture, but there is a greater chance that it will be converted to urban land use (Atalel, 2014; Farooq and Ahmad, 2008; Hofmann, 2001; Koroso, Monica et al., 2021; Mohan, Pathan et al., 2011; Teklemariam and Cochrane, 2021; UN-DESA, 2010). It is a place where the urban system and the rural agricultural system constantly interact and coexist (Adam, 2014a). Neighboring rural zones are the best option for meeting the different needs of people who are dissatisfied with urban territories, jurisdictions, and regulations (Leulsegged, 2011; Mends, 2006; Shishay, 2011), as they are weakly opposed by marginal agricultural activities (Adam, 2014a; Wandl and Magoni, 2016).
According to Ethiopia's national constitution, local small farmers have traditionally retained the majority of the land in the country's peri-urban areas with a lifetime holding right [Federal Democratic Republic of Ethiopia (FDRE, 1995)]. However, because of the push and pull forces, peri-urban lands are under significant pressure to switch to the urban leasehold system (Adam, 2014b; Agegnehu and Mansberger, 2020; Dires, Fentie et al., 2021). Therefore, there is a very high likelihood that town administrations will take peri-urban land and integrate it into an urban territory for habitation and other urban purposes (Koroso, Monica et al., 2021; Shishay, 2011). The constitution and declaration have also backed it. According to expropriation proclamation No. 455/2005 and article 40(8) of the constitution, the government may expropriate private properties for public use where it believes that they were used for better development projects carried out by public entities, private investors, or other organs with payment of compensation (Belachew, 2013; Dires, Fentie et al., 2021). Landowners have, however, expressed dissatisfaction with the local administration's decision about the amount of compensation to be paid, which is too low (Fenta, Yasuda et al., 2017; Teklemariam and Cochrane, 2021). In most cases, rural agricultural land has been converted into urban, built-up property outside the purview of the state apparatus. Several households, especially peri-urban landowners and buyers, participate in the illicit and unofficial conversion of agricultural lands into residential zones (Adam, 2014b, 2020; Tibaijuka, 2004).
The tenure conditions in sub-urban zones were diverse, ranging from the most informal type of occupation to the full-fledged freehold type of ownership (Adam, 2014a; Koroso, Monica et al., 2021; UN-HABITAT, 2018). The prominent area of land owned by informal residents was utilized for cultivation purposes by the local landholders (Adam, 2014a). At this stage, the tenure is formal. When these peri-urban landholders transfer their land to other urban land users, the process goes through informally, and the tenure status changes to informal. Later on, when the local government regularized the land informally occupied by urban residents, its tenure status changed to formal. Hence, the primary actors in informal settlement within the dynamic boundaries of peri-urban zones are the local peri-urban landowners and the needy private sectors. The process operates with three actors: the local land suppliers, the private land users, and the local government. Due to this, both peri-urban landowners and informal land occupiers experience a sense of tenure insecurity.
One reason for the instability of tenure in Ethiopia is the institutional structure of land administration, as rural and urban lands have been managed by distinct institutions, which in turn reduces the possibility of converting the rights of local peri-urban communities into metropolitan terrestrial use privileges. Tenure instability has been brought on by this in the region. Local landowners in peri-urban areas have anticipated they will be liable to expropriation by the government when urban limits approach these areas. As a result, transitional peri-urban areas have a higher prevalence of land tenancy uncertainty in comparison to other spatial units (Adam, 2014b; Teklemariam and Cochrane, 2021).
Consequently, no one is committed to making any kind of long-term improvements to the land in the nonappearance of tenure security. Robe Town is not unique in having this issue. According to our casual observation, peri-urban areas have the highest rates of informal settlement on previously farmed agricultural land. This accelerates the dynamics of variations in land use. Studying such issues with the aid of geo-spatial technologies would help to analyze and visualize the dynamic changes, which helps to make proper urban development planning possible (Al-Rashid, Nadeem et al., 2021; Alwedyan, 2023; Araya and Cabral, 2010; Atalel, 2014; Mallupattu, Kumar et al., 2013; Mohan, Pathan et al., 2011; Verburg, Schot et al., 2004). A summary of the literature is shown in Table 1.
| Title | Methods/Instruments | Theory | Authors |
|---|---|---|---|
| Urban and peri-urban agriculture (2011) | Geo-spatial technologies, Key informant interviews | - | Forkuor and Cofie |
| Urban land policies in developing countries (2012) |
Temporal data from imageries analysed with IDRISI image processing application Software and sprawl index |
Intensification vs. sprawl Or Bid-rent Theory |
Agunbiade et al. |
| Urban resilience (2022) | Socio-economic survey | Resilience theory | Gashu and Alene |
| Farmland protection (2019) | Econometric models and Satellite data | Bid-rent theory and local government’s land supply theory | Huang et al. |
| Agricultural land conversion (2016) | Econometric models | Bid-rent theory and Microeconomic theory | Jiang and Khang |
| Agricultural land (2013) | Descriptive statistics | Von Thünen agricultural land use model and bid-rent theory | Kiita |
| Competing and conflicting interests for peri urban land (2020) | Desk review and case study research | Political economy theory | Adam |
There are few theories that explain urban structure and land use dynamics. Jiang, Li and Zhang (2016) and Jiang, Li , Xiangzheng et al. (2012) employed the microeconomic theory to study urban land use and land cover changes and their impact on agricultural land. The theory views the transformation of cultivated land into urban development as attributed to decisions made by individual land users to increase the benefits obtained from a parcel. Spatially explicit land use models based on microeconomic theory are effective tools for comprehending the spatial and temporal dynamics of land use decisions among individual agents (Jiang, Li and Zhang, 2016). Nevertheless, they do not offer details regarding the cumulative amount of land use change (Verburg, Schot et al., 2004).
Conversely, the urban bid-rent theory overcomes the limitations of the microeconomic theory by explaining the accumulated outcome of land use change. Huang, Xuejun et al. (2019) and Jiang, Li , Xiangzheng et al. (2012) employed the bid-rent theory to project patterns of urban land use change based on the accessibility of plots and their remoteness from the center of a city. The theory was coined by Alonso (1964) and further developed by White (1988) including more variables like income, transport, and endowment of natural resources. Since then, scholars have adopted the urban bid-rent theory to study urban land use dynamics and their effect on land use conversion (Deng, Jikun et al., 2015; Jiang, Li , Xiangzheng et al., 2012). However, as land supply is principally controlled by governments, the bid-rent theory has limited application these days (Huang, Xuejun et al., 2019). Hence, scholars employed the theory of the government's land supply behavior to examine the influence of built-up extension on land use changes (Huang, Xuejun et al., 2019). The theory assumes that the transformation of farmland into built-up area uses has been attributed to the government's decisions and land supply behavior. High revenue-generating concerns of local governments and promotional incentives shape the land supply behavior of the government (Chen, 2016; Wang, 2008). In most developing countries, local governments transform farmland into built-up areas at low compensation and lease them to land developers at a much higher price (Du, 2017). This type of local government land supply behavior significantly affects agricultural land change under rapid urban expansion (Ding, 2011).
The most inclusive and recently exploited analytical framework in the study of peri-urban land use dynamics in developing countries is political economy theory (Fang, 2016). The theory has been developed by researchers in the field of political ecology (Adam, 2020) to investigate land use conversion at various scales. It conceives that land use conversion is a function of power relations among actors (Tilt, 2007). Moreover, the theory contributes to the understanding of the complex social and environmental change resulting from interrelated and sometimes incompatible economic, social, and ecological forces acting at various scales (Forsyth, 2008). It further enables scholars to study how national governments create urban policy and then how provincial, municipal, and village-level managers implement these policies (Fang, 2016). In the framework of Ethiopia, these three sets of players involved in land use transformation and expansion in sub-urban zones are the national government and its land management offices, the private commercial sector, and the native population (Adam, 2020).
The political economy theory has also been used in this research to analyze the changes in sub-urban land use and how those dynamics affect the tenure security of local landowners. The framework aids in the examination of the changing patterns of land use in peri-urban areas, which were sparked by the need for urban infrastructure and service growth among land users, to which the state responded with a policy, and to which the local government reacted by restructuring the town's boundaries. Hence, the physical enlargement of the boundary caused social issues, including unstable tenancy.
Conceptual frameworkAs illustrated on the analytical construct (Figure 1), urban expansion has primarily been driven by population growth, which has been caused by rural-to-urban migration and natural population growth (Bank, 2021). Such urbanization necessitated the expansion of the economy, social services, and infrastructure. In response to such space demands, urban areas grew either in built-up areas or in their boundaries. Such growth has been accomplished formally through planned means or covertly through squatting. In most situations, the former has started deliberately and with planning, whereas the latter develops informally (Adeboyejo, Abolade et al., 2007).
While the spontaneous expansion may have negative environmental and socio-economic effects like decreased agricultural productivity, environmental pollution from solid and liquid waste, and social impacts like displacing residents and upsetting their social structures, the planned expansion may have positive effects by improving market and employment access to the nearby peri-urban areas (Kamete, Tostensen et al., 2001; Satterthwaite, 2003). These detrimental effects had an even greater influence on the peri-urban landowners' sense of tenure security. This calls for formal urban development planning.
Built-up area enlargement and LULC conversion are issues investigated by several authors (Adam, 2014a; Admasu, 2015; Allan, Soltani et al., 2022; Anees, Mann et al., 2020; Araya and Cabral, 2010; Atalel, 2014; Chopra, Singh et al., 2022; Degefu, Argaw et al., 2021; Fenta, Yasuda et al., 2017; Gashu, K. , 2022; Gemeda, Giuseppe et al., 2020; Jiang, Li and Zhang, 2016; Liu, Liu et al., 2020; Rana and Sarkar, 2021; Satterthwaite, 2003; Wubie, de Vries et al., 2020) linking the dynamicity of urban land use with tenure security. The situation of land ownership security among peri-urban populations has not been well studied in relation to land use dynamics in Ethiopia and other parts of the world. Besides, rapid urban expansion is a phenomenon both in large and intermediate cities in Ethiopia, including Robe, due to weak integration of built-up areas and agricultural land use planning, which resulted in unjustifiable land use conversion in the peripheries. This necessitates designing a special form land management at the peri-urban zones. However, there is no study that addressed the need of special land governance at such zones. Hence, the study aimed at investigating the rate, extent, and direction of built-up area growth and its consequent impact on the tenure security of the neighboring communities. It further emphasized exploring boundary and built-up area expansion of the town at the cost of peri-urban land uses like farming, grazing, and vegetation. Therefore, the study has theoretical and applied significance as it explores the interconnection between land use dynamics and tenure security by integrating geo-spatial techniques with socio-economic data analysis.
Thus, the main focus of this study was to examine the spatial expansion of the town and its consequent impact on the tenure security of the peri-urban landholders. It also examines the intervention strategies of the local government to curb the problems associated with tenure insecurity among pre-urban landholders.
The study area, Robe Town, is located in the Bale administrative zone of Oromia Regional State in southeast Ethiopia at latitudes of 02° 30′ 00ʺ N and 7º 10ʹ 50ʺ N and longitudes of 39° 58′ 15ʺ E and 42° 20′ 30ʺ E (Figure 2). The town is located 430 kilometers from Addis Ababa, the nation's capital. The structural plan's current estimated area is 80.2 km2, and the projected area is 94.1 km2 (Robe Town SPM, 2018). The town is ideal for settlement and a variety of economic activities due to the mild climate and relatively flat topography.
The town was established in 1931 and received official legal recognition of the municipality’s administration in 1955. In 1994, it was designated as the Bale zone's administrative center. Since then, the town has had a growth rate that is far faster than its chronological age. In 2004, it was upgraded to a special town that was in charge of the Oromia national regional state. Originally, the town had four administrative kebeles, but this has eventually been restructured to three, numbered 01, 02, and 03, respectively: Beha-Biftu, Oda-Robe, and Chefe-Donsa.
According to exponential population growth projections, the town's population has increased from 44,382 in the 2007 national census to 80,709 in 2020. This is because of the inclusion of some adjacent rural administrative kebeles into the town's jurisdiction. The current household and family sizes are 13,471 and 3.3, respectively [Central Statistics Agency (CSA, 2014)].
Methods of assessing urban expansion and tenure insecurityA mixed research approach with a concurrent triangulation strategy has been used in this study. Quantitative information has been gathered about the socioeconomic diversity of the population and the magnitude of the town's spatial growth. The survey questionnaire produced information on the peri-urban landowners' tenure security. The questionnaire covered demographic, economic, and previous and current farmland size data. It was administered through enumerators. Due to the problem's high prevalence and their proximity to the town, sample respondents for the socioeconomic survey were specifically chosen from two peri-urban kebeles, namely Hora-Boka and Robe-Akababi (Figure 2). Two target groups have been used in the selection process. The first target groups are peri-urban landowners who have lost some or all of their land over the past 30 years to urban growth. The second target category consists of peri-urban informal settlers who acquire land there concurrently. These groups have chosen from the Robe town because they utilize urban land. The sample size was determined using Yamane (1967, p. 886) sample size determination formula, which assumes a finite population and simple random sampling. Therefore, 202 sample household heads were chosen from the two strata using simple sampling (49 peri-urban landholders and 153 informal settlers), and they participated in the study by responding to the survey questions. In addition, four key informants have been purposefully chosen from the land management offices in Sinana woreda and Robe town. Qualitative data regarding the origin of the town, procedures followed, and an effort made to raise awareness among peri-urban landholders during land expropriation was collected through interviews. Hence, this data was used to substantiate the quantitative data.
The method created by Xiao, Shen et al. (2006) has been used to compute the annual rate of urban area expansion.
where: RUE = rate of urban area expansion; UAi+n = urban area/ built up area at time i+n; i = urban area at time i; n = interval of the calculating period in years.
Urban development consumes land by taking it up for use in its plans. Based on a formula used by Cai, Zhang et al. (2020), the land use consumption rate of the town has estimated:
where: LCR = Land use Consumption Rate; Urb(t1) = Total area extent of the urban agglomeration in km2 for past/initial year; Urb(t2) = Total area extent of the urban agglomeration in km2 for current year; Y = Number of years between the two measurement periods; LN = Natural Logarithm.
The population growth rate of the town has calculated using the formula used by Cai, Zhang et al. (2020):
where: PGR = Population Growth Rate; Pop(t1) = Total population within the city in the past/initial year; Pop(t2) = Total population within the city in the current/final year; Y = Number of years between the two measurement periods.
The ratio of annual land use consumption rate to population growth rate (LCRPGR) has calculated using a formula adapted from Cai, Zhang et al. (2020):
The spatial data used for analyzing the magnitude, speed, and bearing of the spatial growth of the town has been obtained from various sources. While the town's 1984 area has been obtained from the national housing and population census database, the administrative border of the town for 2007, 2020, and the proposed area have been obtained from the town's municipality in an AutoCAD format. Such information is not available as a map for 1994. The LULC dynamics of the area were assessed using Landsat images from MSS 4/5 (Multi Spectral Sensor), TM 7 (Thematic Mapper) and OPI/TIRS 8 (Operational Land Imager/Thermal Infrared Sensor) of 1984, 2002, and 2020, respectively, which were obtained free from the United States Geological Survey (USGS) portal. The data was extracted with path 167 and row 55 and clipped with the boundary of the area of interest. Pre-processing techniques for image enhancement have been employed in the ERDAS IMAGINE platform, and then LULC classifications have been made (Figure 3).
Following this, pixel-based supervised classification with the maximum likelihood algorithm has been employed to classify the images into four land use classes, namely built-up area, farmland, vegetation, and grassland. To display the enlargement of the built-up area, these LULC categories have been further reclassified into built, open, or none-built areas. Their respective definitions are presented in Table 2. ArcGIS has employed these techniques in processing the images, calculating area, detecting change, and adding layout to the maps.
| LULC classes | Description of the classes |
|---|---|
| Built-up area | This includes land used for residential homes, businesses, institutions, and industries, and infrastructure like roads and ditches. |
| Farmland | Included are both cropland and the acreage that is being prepared. |
| Vegetation | Areas covered by both native and planted trees and shrubs are included. |
| Grassland | Included are areas covered in grass in open fields, on the sides of roads, and in yards. |
| Open Space/unbuilt | This includes undeveloped territory such as the open countryside, forests, fields of crops, parks, undeveloped urban areas, and cleared land. |
A post-classification technique for assessing precision and the kappa coefficient has been developed. To this effect, equal training areas (20 for each) have been collected for the four LULC classes. Historical data from Google Earth has been used as reference data for the classification of 1987 and 2002 images. In doing so, 80 random points were generated and converted to KML by using Arc GIS to display on the Google Earth map. The values of these random points were verified from the Google Earth map and compared with the values of the classified map for accuracy assessment (Abineh and Bogale, 2015). Ground truth data collected with Garmin 72 handheld Global Positioning System (GPS) was used to assess the accuracy of the 2020 LULC classification. It has found the overall accuracy assessment of the classification for the years 1987, 2002, and 2020 to be 87.34%, 87.50%, and 88.75%, respectively. The values of the kappa coefficient for the above years are 0.82, 0.83, and 0.84, respectively (Appendix 2).
Oral traditions publicized that the town's original core had been established at the location of the current kebele 01 (Beha-Biftu). The key informants stated that this area had formerly been home to an agrarian populace. Later, as traders started to reside in the area, the market (Gabaa Togaa) that had been established nearby in 1931 gradually transformed the area into a commercial hub. The town has been expanding with its physical size and population since it was founded as a village in different directions. Such information gives insight into the situation of tenancy in the peri-urban rural kebeles, as the rate and direction of the town’s growth have implications on the sense of tenure security of the adjacent landholders.
The analysis result presented in Table 3 was calculated with the aid of equation 1. The result publicized that the town's area expanded annually at rates of 18.6%, 11.3%, and 9.3% for the years 1984 to 1994, 1994 to 2007, and 2007 to 2020, respectively. This outcome is comparable to Bahir-Dar's expansion rate, which was 12%, 14%, and 5% from 1957 to 1984, 1984 to 1994, and 1994 to 2009, respectively (Fenta, Yasuda et al., 2017). From 5.1 square kilometers in 1984 to 80.2 square kilometers in 2020, the area exhibited an annual increase of 40.9%. This suggests that between 1984 and 2007, 9.5 km2 of land was taken from nearby peri-urban landowners. Between 1994, 2007, and 2007 and 2020, respectively, 21.6 km2 and 44 km2 of land were taken from peri-urban landowners. In sum, 75.1 km2 of land were expropriated from peri-urban landowners over the previous 33 years (1984–2020). The survey of 49 peri-urban landowners who lost entirely or a portion of their farmland found that they were forced to vacate their properties for urban development due to the town's horizontal expansion.
| Year | Rate of urban expansion | LCR | LCRPGR |
|---|---|---|---|
| 1984 | - | - | - |
| 1994 | 18.6% | 0.105 | 4.2 |
| 2007 | 11.3% | 0.069 | 1.25 |
| 2020 | 9.3% | 0.061 | 1.24 |
Results in Table 4 showed that 48.9% of the land owners lost around half of their farmland, while 34.7% lost nearly a quarter of it. 16.3% of them lost about three-fourths of their farmland for urban land usage. Additionally, there was an intention to expand the town's area in the near future to 94.1 km2. Such territorial expansion happened at the cost of the neighboring rural administrative kebeles, particularly Robe Akababi and Hora-Boka rural administrative kebeles (RAK) (Figure 2). Though such household heads expropriated their plot with insufficient compensation, some of them have gotten economic benefit by shifting their means of sustenance from primary agricultural activities to non-agricultural service-based activities.
| Size of farm land expropriated | Total | ||
|---|---|---|---|
| 1/4 of the farm land | 1/2 of the farm land | 3/4 of the farm land | |
| 2 | 0 | 0 | 2 |
| 7 | 7 | 3 | 17 |
| 8 | 14 | 3 | 25 |
| 0 | 3 | 2 | 5 |
| 17 | 24 | 8 | 49 |
Results in Table 3 calculated with the use of Equation (2) showed that the town's LCR is high with a trend toward decline over the years 1984 and 1994, 1994 and 2007, and 2007 and 2020, respectively, with values ranging from 0.105 to 0.069 to 0.061. The result disclosed that the town appropriated peri-urban land at annual rates of 10.5%, 6.9%, and 6.1% for other urban uses. The LCRPGR value calculated using Equation (4) (value greater than 1) indicated inefficient land use practices in the town with a low population per area ratio. The trend showed a gradual improvement in land use efficiency from 1987 (4.2) to 2020 (1.24). This implies the possibility of in-fill development rather than outward extension.
Regarding expansion direction, up until 1984, the town had grown in all directions around its original nucleus. Since then, the expansion has continued to grow because of the construction of new roads in a north-south orientation (Figure 4). The town has further expanded in the direction of the south, which is an exit to Goba town. This is primarily due to the fact that the fact that land in this direction has been cleared of cultivation (previously a state farm) and left for grazing, the establishment of numerous administrative offices, and the relocation of the bus station and new market (Gabaa Doonsaa) from the central to the southern parts of the town.
In 2007, the boundary was extended to the west part of the town, towards the neighboring rural administrative kebele of Hora-Boka. However, the farmland in this direction is highly productive and has a high market value. Because of this, there has been increasing opposition to the town's expansion in this direction. Nevertheless, expansion has reached the productive farmlands in the western direction after extensive negotiation between the peri-urban landholders who cultivate the plot and the municipality of the town (Interview with the Mayor, 2020). However, this year's eastern growth is severely constrained due to the close proximity of the Robe-Akababi Farmers' Association residential village to the town. Consequently, the town grows along the roadside in a linear form (Figure 4). Accordingly, the settlement takes on an elongated shape.
The horizontal expansion occurred in all directions in 2020. In addition to the west and north-south expansions, a major expansion occurred in the east, which had previously resisted the expansion. This is mostly because the town's jurisdiction now extends to the Robe-Akababi rural administrative kebele (Figure 4).
Magnitude and direction of built-up area expansionAn assessment of the horizontal extension of the built-up areas has been made using land-sat images from 1987, 2002, and 2020. The direction and magnitude of the spatial expansion of the built-up area have been analyzed on the Arc GIS platform using the mean center of the 1987 map as a reference point.
The findings indicated that in 1987, the southern direction (1.8 km), the north-northwest (1.67 km), and the northern directions had the greatest spatial increase of the built-up area (1.25 km). West-Southwest (0.25 km), East (0.28 km), and East-Northeast (0.3 km) directions experienced the least expansion (Figure 5 and Appendix 1).
The southern direction experienced the largest horizontal extension of 3.06 km in 2002. South-Southeast and West-Southwest, each with 2.12 km, have come after it. Additionally, 2.08 km of expansion have been made in the westward direction (Figure 6).
The built-up area moved 6.62 kilometers to the south-southwest in 2020. It has been followed by a distance of 5.07 km in the south and 3.46 km in the north, respectively (Figure 7).
As a result, over the past 33 years, the built-up area has expanded most rapidly in a southerly direction, at a distance of 9.91 kilometers from the town's mean center. After that, it continued on a course that was 8.84 kilometers southwest of the mean center. Additionally, it extends for 6.56 km and 6.44 km in the north-northwest and northern directions, respectively (Figure 8).
Examining the LULC dynamics of the town helps to get an insight into the magnitude of land use alteration made because of the town's expansion to its neighboring rural administrative kebeles. The town is surrounded by rural administrative kebeles that rely on agricultural activities as a means of livelihood. Though there are debatable ideas regarding the influence of urban expansion, it undoubtedly triggers tenure insecurity among these peri-urban residents. Thus, two rural administrative kebeles have been considered for this purpose. The land use and cover of the town have been classified into four categories: built-up, agricultural, vegetative, and grassland (Table 2).
In 1987, the built-up area made up 5.46% of the total area, compared to 39.6% of farmland and 20.3% of vegetation. 35.2% of the total area within the town boundaries is made up of grassland (Figure 9 and Table 3).
Therefore, the majority of the area is classified as farmland, followed by grassland. The year 2002 saw 12.7% growth in the built-up area and a 47.7% increase in agriculture. On the other hand, vegetation and grassland declined to 18.8% and 16.7%, respectively (Figure 10 and Table 5).
| Year | 1987 | 2002 | 2020 | |||
|---|---|---|---|---|---|---|
| LULC classes | area (km2) | % | area (km2) | % | area (km2) | % |
| Built up | 4.38 | 5.46 | 10.2 | 12.7 | 27.9 | 34.8 |
| Farm land | 31.3 | 39 | 38.3 | 47.7 | 26.9 | 33.5 |
| Vegetation | 16.3 | 20.3 | 15.1 | 18.8 | 9.99 | 12.5 |
| Grass land | 28.2 | 35.2 | 16.7 | 20.8 | 15.4 | 19.3 |
| Total | 80.2 | 100 | 80.2 | 100 | 80.2 | 100 |
This suggests that the process of changing the area's LULC is dynamic. Furthermore, the addition of two rural kebeles (Robe Akababi and Hora-Boka) into the urban boundaries has contributed a lot to the high rate of LULC conversion in the surrounding area.
Change detection analysis has been performed on the ArcGIS and Excel platforms to determine which land use or land cover class has shifted (Figure 11 and Figure 12).
The result in Table 6 indicates that the built-up area increased over time. The mean gain in built-up area between 1987 and 2002 was 38.8% per year. It reached 98.3% between the years 2002 and 2020, which is about half the mean built-up area expansion rate of Addis Ababa city, 180% (Teklemariam and Cochrane, 2021). The built-up area reached a climax (17.7 km2) between 2002 and 2020.
| Year | Built-up area (km2) | Gains in built-up area (km2) | Mean gain per year |
|---|---|---|---|
| 1987 | 4.38 | - | - |
| 2002 | 10.2 | 5.82 | 38.8% |
| 2020 | 27.9 | 17.7 | 98.3% |
Between 1987 and 2002, 16.3 km2 of land was converted from grassland to farmland (Table 7). This contributed to the increase in farmland in 2002. Furthermore, three LULC categories of varying magnitude have transformed into built-up areas. Areas of 2.2 km2, 2.6 km2, and 3.7 km2, vegetation, grassland, and farmland have changed to built-up areas, respectively.
| LULC | 2002 | |||||
|---|---|---|---|---|---|---|
| Built up area | Farm land | Grass land | Vegetation | Total | ||
| 1987 | Built up area | 1.5 | 1.7 | 0.6 | 0.5 | 4.4 |
| Farm land | 3.8 | 13.9 | 5.8 | 5.2 | 28.7 | |
| Grass land | 2.6 | 16.3 | 7.1 | 5.0 | 30.9 | |
| Vegetation | 2.2 | 7.0 | 2.6 | 4.4 | 16.2 | |
| Total | 10.1 | 38.9 | 16.2 | 15.1 | 80.2 | |
Hence, the built-up area consumed much more farmland compared to the other LULC categories. This is concomitant with the findings of (Attua and Fisher, 2011); Mohan, Pathan et al. (2011) and Mallupattu, Kumar et al. (2013).
Between 2002 and 2020, 5.9 km2 of vegetation, 7.2 km2 of grassland, and 8.8 km2 of farmland were converted to built-up areas (Figure 12 and Table 8). Here also, the dominant provider of land for built-up land use is farmland.
| LULC | 2020 | |||||
|---|---|---|---|---|---|---|
| Built up area | Farm Land | Grass Land | Vegetation | Total | ||
| 2002 | Built up area | 6.0 | 2.0 | 1.5 | 0.5 | 9.9 |
| Farm land | 9.6 | 18.1 | 9.5 | 2.3 | 39.5 | |
| Grass land | 7.2 | 3.5 | 2.7 | 2.6 | 16.0 | |
| Vegetation | 5.9 | 2.9 | 1.5 | 4.5 | 14.9 | |
| Total | 28.7 | 26.4 | 15.2 | 9.9 | 80.2 | |
Between 1987 and 2020, the built-up area of the town grew at the cost of farmland, grassland, and vegetation. They contributed 10.2 km2, 9.3 km2, and 5.9 km2 of area, respectively (Table 9). Here also, the built-up area consumed much more farmland in comparison to the other LULC categories.
| LULC | 2020 | |||||
|---|---|---|---|---|---|---|
| Built up area | Farm land | Grass land | Vegetation | Total | ||
| 1987 | Built up area | 2.6 | 0.9 | 0.6 | 0.3 | 4.4 |
| Farm land | 10.9 | 8.6 | 6.3 | 3.4 | 29.2 | |
| Grass land | 9.3 | 12.1 | 5.5 | 3.8 | 30.7 | |
| Vegetation | 5.9 | 4.8 | 2.8 | 2.5 | 16.0 | |
| Total | 28.7 | 26.4 | 15.2 | 9.9 | 80.2 | |
Hence, the dynamic conversion of agricultural lands to urban land uses was induced by the hasty enlargement of the built environment to the adjacent peri-urban area. This, along with the boundary extension of the town, leads to the insecurity of peri-urban land owners’ tenure due to fear of expropriation and eviction. Hence, the physical change of the town may lead to a change in the urban socio-spatial structure too (Amirfakhria, 2022).
Tenure security of peri-urban landholdersThe survey result revealed that the dominant suppliers of land in the peri-urban area were farmers, urban residents, and brokers and speculators. Regarding the legality of the transaction, about 99.5% of them (suppliers and users) assert it is informal. Limited access to official land acquisition by urban inhabitants, the high market value of formal land transactions, and speculation that land values will rise are major reasons that attract land users to participate in informal land transactions. The majority of peri-urban land users (buyers) hope that the municipal administration will regularize their informal plots. In the study, it was identified which factors compelled the land supplier in the peri-urban area to participate in the informal land transaction. These include the absence of a legal method for converting agricultural land into urban land use, an overly bureaucratic legal process for doing so, low compensation in formal land transactions, concern over the expropriation of farmland, the availability of lucrative pay in the black market, and broker agitations. The average price of a plot (200 m2) in the sub-urban area, according to the report of land users in the informal market, is 78,000 Ethiopian Birr (ETB), while the land suppliers reported 97,000 ETB. However, 67,000 ETB is the average compensation received for taking 200 m2 of peri-urban land in the formal market (Interview with Sinana Woreda, Rural Land Use, and Management Office, 2022).
Additionally, the size of the plots available in peri-urban informal transactions is generally larger than the supply from legitimate sources. The minimum lot size available in informal transactions is 200 m2, but it is 160 m2 through the formal route. Because of the interaction between supply and demand, peri-urban landowners were parties to informal land deals. This in turn makes both the land provider’s and the user's tenure more precarious. The survey's findings showed that 51.6% of land users are insecure about their plots and any dwellings they have built without proper permits because they are afraid that the owners will come after them and that the government will take away their properties. Due to their involvement in the black market, about 87.2% of land providers (peri-urban landholders) felt insecure because they feared being expropriated by the town administration with minimal compensation and punished by the government. The town's boundary has frequently been restructured, and the resulting horizontal enlargement of the town ensures that their sense of insecurity has its own ground.
A study made in Wolaita Sodo town by Zerihun (2020) found that 97% of peri-urban households are concerned about their landholdings. This finding is also concurrent with the findings of Adam (Adam, 2020), who recognized that the majority of the local landowners in the peri-urban area have been forced to sell their plots in the informal market due to the rapid urban development in the peri-urban areas and the accompanying tenure instability brought on by government-led land acquisition. Likewise, the study results of Teklemariam and Cochrane (2021) reported that territorial expansion of built-up areas to the adjacent agricultural community resulted in perceived tenure insecurity in Addis Ababa and Hawasa cities.
The finding has theoretical implications in that it advances existing theoretical literature, particularly the theory of political economy. In the study, it was found that frequent restructuring of the town’s boundary is a response given by the municipality (local government) to the ever-increasing need for land for residential, industrial, commercial, and institutional usage by the private sector (market). Such a demand for land is satisfied with land owned by the peri-urban community. Thus, like other areas of the world (Fang, 2016; Forsyth, 2008; Tilt, 2007) and Ethiopia (Adam, 2020), the three actors (state, market, and local community) explain the land use dynamics of Robe town and its consequent impact on the tenure security of the local community. The multifaceted power relations and socio-economic interactions of the three actors were vividly observed in that the federal government enacted the leasehold policy to govern urban land and the local government frequently restructured the town’s boundary to respond to the ever-increasing necessity for urban land and generate more revenue from tax. Such restructuring and extension of the town boundary encountered resistance from the peri-urban landholders. Such incongruity is usually resolved through negotiation between the local government and the indigenous community. In such an instance, the town administration was forced to reconsider its strategy. One such peculiar strategy devised by the Robe town administration was 40/60, a strategy that allotted 40% of the farmland to the peri-urban landholder and 60% to the town administration. Moreover, the empirical findings of the study are also consistent with the thoughts of the political economy theory. We thus recognized the application of the theory to understand the multifaceted issues of urban land use dynamicity and its effect on the physical (farmland) and social (tenure insecurity) environment of the adjacent agricultural community.
Methodologically, the study addresses the two major dimensions of urban expansion (Wubneh, 2018). The first is the spatial expansion of the town that has an adverse effect on the adjacent farming land that has been addressed with the aid of spatial data analysis techniques. This method enables us to comprehend the magnitude and direction of the physical growth of the town. The second is its socio-economic consequences, which have been addressed through socio-economic surveys and data analysis. This helps to understand the level of economic consequence due to the absorption of farmland by urban development endeavors Fenta, Yasuda et al. (2017) as well as the social consequence extended to the surrounding agricultural community due to a sense of tenure insecurity. Thus, the study addresses these two concerns concurrently by integrating spatial and socio-economic data analysis techniques.
Furthermore, the study identified the speed and direction of the town’s expansion as well as the rate and types of land use conversion. This, in turn, could help policymakers design strategies that appropriately support compacted urban growth. Additionally, the outcome aids in the establishment of policies that benefit both efforts at urban development and the enhancement of rural livelihoods. It further benefits local decision-makers design strategies that reduce informal land transactions resulting from tenure insecurity and haphazard land use practices. Empirical observation of the study depicted that the built-up area is much smaller than the boundary of the town (Figure 7). Hence, it helps the municipality and land use managers of Robe town to consider infill development rather than frequent restructuring (Figure 4) of the town’s boundary.
The results indicate that the town of Robe is rapidly expanding to include the nearby rural administrative kebeles. Between 1984 and 1994, 1994 to 2007, and 2007 to 2020, the town's area expanded at yearly rates of 18.6%, 11.3%, and 9.3%, respectively. The town took 75.1 km2 of land from peri-urban farmers between 1987 and 2020. The ratio of land consumption to population increase revealed the town's inefficient land use practices, despite the fact that the rate of land consumption is on the decline. The south was the direction in which the built-up area expanded the most, at 9.91 km from the average center of town. The tenure security of peri-urban farmers has been affected in some way by this unchecked horizontal expansion and poor planning system. Rapid urbanization, which generated high demand for urban land for housing, has tested and greatly altered the security of tenure for peri-urban farmers. This may be seen in the quick rate at which agricultural land is converted to urban areas. Over the course of 33 years, 22.7 km2 of farmland (or 42% of the agricultural land) have transformed into built-up areas (9.2 km2 in 1990 to 26.7 km2 in 2020). Such alterations in land use occurred primarily in the peri-urban zone, within unregulated land transaction markets. Although there are several variables that contribute to the peri-urban informal land market, the main one is the expropriation of peri-urban land with insufficient compensation. This expands the town's and built-up area's limits to the frontiers of cultivated land at the periphery, adding to peri-urban farmers' sense of insecurity about their landholdings.
In general, the land management system should consider a policy that equally benefits the three actors: the state that generates revenue from urban development, the private sector that utilizes land for various ends, and the peri-urban land owners that supply land for such usages. As the nature of peri-urban land management is quite differ from both urban and rural land management system, a exceptional land management policy ought to be designed by such policy makers. Hence, informed land management policies should be designed by the government at the federal, regional, state, and local levels. Specifically, the town's land management should improve its land use efficiency through in-fill development and vertical expansion of built-up areas. Moreover, the current regulation of compensation rates has been revised in a way that benefits landowners in peri-urban areas (at least better than the informal market). Finally, the researchers suggested further study on the land use efficiency of Robe Town with the aid of advanced spatial data.
Conceptualization, Getachew; methodology, Getachew; software, Getachew; investigation, Getachew and Zeleke; resource, Zeleke; data acquisition, Zeleke; writing-original draft, Zeleke & Getachew; writing-review and editing, Zeleke; visualization, Getachew. Both authors have read and agreed to the published version of the manuscript.
The authors declare that they have no conflicts of interest regarding the publication of the paper.
We are indebted to all individuals who participated as field guides, enumerators, and respondents to the study.
This study received no specific funding from public, commercial, or not-for-profit funding agencies.
| Direction | 1987 | 2002 | 2020 | Grand Total |
|---|---|---|---|---|
| E | 0.28 | 0.88 | 2.46 | 3.62 |
| ENE | 0.3 | 0.79 | 2.32 | 3.41 |
| ESE | 0.48 | 0.9 | 2.81 | 4.19 |
| N | 1.25 | 1.73 | 3.46 | 6.44 |
| NE | 0.33 | 0.48 | 1.72 | 2.53 |
| NNE | 0.46 | 1.05 | 2.17 | 3.68 |
| NNW | 1.67 | 1.86 | 3.03 | 6.56 |
| NW | 0.62 | 1.07 | 2.12 | 3.81 |
| S | 1.8 | 3.06 | 5.07 | 9.93 |
| SE | 0.51 | 0.88 | 2.2 | 3.59 |
| SSE | 0.65 | 2.12 | 2.78 | 5.55 |
| SSW | 0.85 | 1.37 | 6.62 | 8.84 |
| SW | 0.56 | 1.81 | 2.9 | 5.27 |
| W | 0.49 | 2.08 | 1.7 | 4.27 |
| WNW | 0.34 | 1.05 | 1.84 | 3.23 |
| WSW | 0.28 | 2.12 | 3.26 | 5.66 |
| Grand Total | 10.87 | 23.25 | 46.46 | 80.58 |
ACCURACY TOTALS (1987)
----------------
Class Reference Classified Number Producers Users
Name Totals Totals Correct Accuracy Accuracy
---------- ---------- ---------- ------- --------- -----
Built up area 11 11 11 100.00% 100.00%
Grass Land 27 31 24 88.89% 77.42%
Vegetation 20 17 14 70.00% 82.35%
Farm Land 21 20 20 95.24% 100.00%
Totals 79 79 69
Overall Classification Accuracy = 87.34%
----- End of Accuracy Totals -----
KAPPA (K^) STATISTICS
---------------------
Overall Kappa Statistics = 0.8253
ACCURACY TOTALS (2002)
----------------
Class Reference Classified Number Producers Users
Name Totals Totals Correct Accuracy Accuracy
---------- ---------- ---------- ------- --------- -----
Grass Land 18 17 15 83.33% 88.24%
Built up Area 15 10 10 66.67% 100.00%
Vegetation 24 22 22 91.67% 100.00%
Farm Land 23 31 23 100.00% 74.19%
Totals 80 80 70
Overall Classification Accuracy = 87.50%
----- End of Accuracy Totals -----
KAPPA (K^) STATISTICS
---------------------
Overall Kappa Statistics = 0.8299
ACCURACY TOTALS (2020)
----------------
Class Reference Classified Number Producers Users
Name Totals Totals Correct Accuracy Accuracy
---------- ---------- ---------- ------- --------- -----
Built up Area 31 28 27 87.10% 96.43%
Grass Land 9 8 5 55.56% 62.50%
Farm Land 24 28 23 95.83% 82.14%
Vegetation 16 16 16 100.00% 100.00%
Totals 80 80 71
Overall Classification Accuracy = 88.75%
----- End of Accuracy Totals -----
KAPPA (K^) STATISTICS
---------------------
Overall Kappa Statistics = 0.8411
