Conference Proceedings

11th Esri Eastern Africa User Conference

2 - 4 November, 2016

Acacia Premier Hotel, Kisumu, Kenya Table of Contents Foreword ...... 7 Natural Resources ...... 8 GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya ...... 9 Abstract ...... 9 Introduction ...... 10 Examples of structured and unstructured ...... 10 National Data Center (NDC) ...... 10 NDC System ...... 11 NDC Architecture ...... 12 NDC Functionality ...... 12 ArcGIS System...... 12 Conclusion ...... 17 Refrences ...... 17 Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools ...... 18 Abstracts: ...... 18 Problem Definition/ Background ...... 19 Methods/Methodology...... 19 Expected outcomes (Results and major findings) ...... 21 Biological references ...... 21 Biographical Notes ...... 22 Contacts ...... 22 Impact of Climate Change on Desertification in Arid Areas of Kenya ...... 23 Abstract ...... 23 Introduction ...... 24 Aridity ...... 24 Climate change...... 24 Desertification...... 25 Droughts causes and desert spread...... 25 Magnitude of Desertification in Africa ...... 26 Causes of Desertification...... 26

i Objectives of Study ...... 26 Methodology ...... 27 Description of study site ...... 27 Results and Findings ...... 32 Findings ...... 33 Interventions to Avert Future Occurrences ...... 33 Acknowledgement ...... 34 References ...... 34 Contacts ...... 36 Local Government ...... 37 A Village Level GIS-Based County Government and Environmental Risk Management Decision Support System ...... 38 Abstract ...... 38 Introduction ...... 39 Methodology ...... 40 Area of study ...... 40 Geodatabase Construction ...... 40 Chlorine gas plume footprint modeling and analysis ...... 42 Results and Discussion ...... 44 Household Proximity to infrastructure ...... 44 Environmental Risk Analysis and Management ...... 45 Conclusion and Recommendations ...... 47 Reference ...... 47 GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste Disposal Site: A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania...... 48 Abstract ...... 48 Introduction ...... 49 Mapping and Site Characterizations of Existing Situation ...... 49 Location ...... 49 Description of Pugu dumpsite ...... 49 Water supply ...... 50 Social services ...... 50 Existing land use patterns ...... 50 Economic Activities ...... 50

ii Hydrological conditions ...... 50 Solid waste existing practice ...... 51 Material and Method ...... 52 Result and Discussions ...... 52 Pugu Dump Site Pollution Modeling, Risk Analysis and Water Demand ...... 53 Pollution modeling ...... 53 Groundwater flow predictions ...... 53 Modeling analysis and assumptions ...... 54 Modeling development, assumption and Output ...... 54 Output of the model ...... 55 Sensitivity of the model ...... 58 Conclusion and Recommendations ...... 59 Conclusion ...... 59 Recommendations ...... 59 Reference ...... 59 Comparing two geospatial approaches for delineating crop ecologies in Tanzania ...... 62 Abstract ...... 62 Introduction ...... 63 Methods ...... 63 Study area ...... 63 Statistical analysis ...... 64 Study area and the data ...... 64 Data exploration and analysis ...... 66 Results ...... 67 Selecting the best performing integrated technology ...... 67 Top-down approach ...... 68 Bottom-up approach ...... 69 Discussion...... 72 Differences in approaches ...... 72 Relevance ...... 73 Limitations ...... 73 References ...... 74 Contacts ...... 74

iii Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North- Central Nigeria ...... 75 Abstract ...... 75 Introduction ...... 76 Study Area ...... 76 Research Methodology ...... 79 Data Acquisition ...... 79 Image Geometric Correction ...... 80 Data Analysis...... 80 Conclusion ...... 84 References ...... 85 Contacts ...... 85 National Government ...... 86 A Geographic information System driven integrated land management System ...... 87 Abstract ...... 87 Introduction ...... 88 Problem Statement ...... 89 Integrated Conceptual Framework ...... 90 Implementation Strategy ...... 90 Implementation progress ...... 91 GIS Integration ...... 93 Conclusion ...... 93 Reference ...... 94 Challenges of Developing Land Information Management Systems (LIMS) for County Governments in Kenya ...... 95 Abstract ...... 95 Introduction ...... 96 Methodology ...... 96 Results and Findings ...... 97 Legal challenge ...... 97 Social challenges ...... 98 Political challenges ...... 98 Technical challenges ...... 98 Economic challenges...... 99

iv Conclusion ...... 100 References ...... 100 Biographical Details ...... 102 Contacts ...... 102 Utilities & Transportation ...... 103 Ves Sites Selection Model for Ground Water Analysis and Mapping ...... 104 Abstract ...... 104 Introduction ...... 105 Study Area ...... 105 Datasets and Methodology ...... 107 Datasets ...... 107 Methods...... 107 Results and Discussions ...... 111 Conclusions...... 115 Acknowledgment...... 116 References ...... 116 Biographical Notes ...... 117 Contacts ...... 117 Cross-Cutting Issues ...... 118 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example ...... 119 Abstract ...... 119 Introduction ...... 120 More Recent Activities ...... 121 A Web GIS-based viewing tool on forest use history ...... 121 Developing environmental education tools ...... 123 Streamlining GIS teaching across universities ...... 124 Conclusions ...... 126 References ...... 126 Acknowledgements ...... 128 Biographical Notes ...... 128 Contacts ...... 128 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda ...... 129 Abstract ...... 129

v Introduction ...... 130 Methodologies ...... 130 Methodology Using Site Selection (Environment) and GIS ...... 130 Methodology Using SD ...... 138 Results: Geoportal Creation ...... 139 Conclusion ...... 140 References: ...... 140 Biographical notes: ...... 141 Contacts: ...... 141

vi Natural Resources

Use smart maps and spatial analysis to better manage the earth's natural resources. Increase production, optimize workflows, and mitigate risk. GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

Diana Matee, Kenya

Abstract The petroleum industry is an information led business in which the market capitalization of any oil companies is mainly dependent on an expectation of the value of future production. This number depends entirely on interpretation and understanding of datasets about resources that are both hidden far below earth’s surface and are often also in remote inaccessible locations. Oil companies are not unique on how much they rely on information, but E&P is one of the activities where the financial impact of data is highest. Therefore, data and information management is crucial for the success of any oil company. National Oil Corporation of Kenya as a custodian of all the oil and gas data, it embarked on a process of implementing a National Data Center (NDC). NDC is a centralized dynamic system that manages and preserves a country’s petroleum data assets with diverse set of data management tools such as automated, quality assured workflows and services that help to encourages external investment. Most of the data in NDC has spatial component to them such as well coordinates, seismic line location or a regional span survey the design of NDC utilizes GIS as the defector spatial tool.

Natural Resources Track 9/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Petroleum industry just like most of research based industries is an information led business .In order to capitalize on the market, companies mainly depend on an expectation of the value of future production. This value depends entirely on the interpretation of data about resources that are location based.

The data can be categorized into-structured and unstructured data. Structured data which is organized geoscientific data e.g. seismic, well logs, gravity and magnetic data just to mention a few, whereas unstructured data entails mostly support reports for the geoscientific data.

Examples of structured and unstructured

• Geological - surface & sub-surface geology maps • Geophysical - Seismic, gravity, magnetic, Structured data navigation data • Petro physical -logs, cores, cuttings • Geochemical -fluid samples

• Reports e.g. geological reports, seismic sections images,core images e.t.c Unstructured data

To manage this National Oil Corporation of Kenya with the Ministry of Energy deployed a National Data Center.

National Data Center (NDC) A National Data Center (NDC) is a platform that enables archival and retrieval of regional, national, or governmental exploration and production data.

It manages a country’s petroleum resources by preserving data resources through centralizing the data. Moreover it assists in promoting more investments to and from the data by having a safe and secure platform where investors can view and buy data. In addition to increasing collaboration among government bodies, international oil companies (IOC) together with research centers.

The country’s NDC works flow is dynamic such that is caters for the different data formats ranging from physical to softcopy.

The below diagram shows the work flow of the NDC

Natural Resources Track 10/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya NDC Structure

Physical data Data access assets environment

Data management environment

Offsite backup Digital database

Data is received whether physical or digital, it is taken through data management process where the digital data is store in a database and the physical in cabinets.

NDC System The NDC system comprises of two systems that run concurrently:  ProSource Enterprise System, entails;  ProSource Data Service (PDS) application which is an online interface to access the data.  ProSource server which manages the data.  Seabed database which stores non spatial data.  ArcGIS Enterprise System entails;  ArcGIS portal which is an online application where all the published content can be accessed.  ArcGIS server where data in published.  ArcSDE database where spatial data is stored.

Natural Resources Track 11/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya NDC Architecture

User

ProSource Data ArcGIS Online Service Client ArcGIS

Desktop ArcGIS System ProSource System ProSource ArcGIS Server Server

Seabed ArcSDE DB DB

NDC Functionality All the data types are stored in the database, which comprises of seabed and ArcSDE oracle based databases. The unstructured data is stored in the Seabed databases (seabed db) while the structured data is stored on ArcGIS database (ArcSDE) which have a spatial entity to it.

For the ProSource System data is fetched from the database and managed by the ProSource sever and can be accessed by the user through ProSource Data Service Client (PDS).

ArcGIS System. Spatial data is stored in the ArcSDE database, it is the fetched by the ArcGIS Desktop application from the connection, as shown by the image below;

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

This data is used to populate a map, as shown below;

Natural Resources Track 13/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

The map is then published to the ArcGIS server;

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Then the link is redirected to the ProSource Data Service client for view and also the ArcGIS Portal;

Natural Resources Track 15/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

And the portal for data mining purposes;

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Conclusion ArcGIS in general is a powerful tool for not only mapping but also visualization. In National Oil Corporation of Kenya it’s being utilized to not only map our data resources but due to its versatility we are able to build upon it other systems that can be used to better manage our resources. In addition to this we are able to centralize all our tools and resources helping to easily data management and resource utilization. Refrences The Management of Oil Industry Exploration & Production Data by Steve Hawtin

Natural Resources Track 17/141 Diana Matee GIS Utilization in Petroleum Data Management, A Case Study of National Data Centre (NDC) in National Oil Corporation of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools

Noreen MUTORO1, Kenya; Gertrud SCHAAB2, Germany; Mary WYKSTRA1, Kenya 1 Carnivores, Livelihoods and Landscapes; Acton for Cheetahs in Kenya (ACK), 2 Karlsruhe University of Applied Sciences, Karlsruhe

Keywords: Gene flow, Range wide Planning, Remote Sensing, Species Occupancy, Species Distribution

Abstracts: Action for Cheetahs in Kenya (ACK) is the only range-wide cheetah conservation organization in Kenya. ACK conducted the first Kenya national cheetah survey in collaboration with the Kenya Wildlife Service, Cheetah Conservation Fund and East African Wildlife Society between 2004 and 2007. We were the first to create a range-wide map of cheetahs based on actual site visitation across the entire country. Results of the survey formed the baseline for national and regional strategic planning. Methodology for the second survey will include land cover and anthropogenic influences mapped in ArcGIS with cheetah occupancy from field surveys, detection dog scat collection and gene flow analysis. This presentation will highlight the changes being made from the first survey in order to assure improved knowledge to influence cheetah conservation strategies. Occupancy modelling and genetic mapping will be used to map and analyse trends in cheetah status and genetic flow between populations. Detection dogs will locate scat to evaluate prey selection, cheetah health and genetic variability. Remote sensing technology will test assumptions on land use change affecting cheetah habitat. Results of pilot studies conducted between December 2015 and August 2016 form the framework for completing the range-wide GIS-based evaluation.

Natural Resources Track 18/141 Noreen Mutoro, Gertrud Schaab, Mary Wykstra Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Problem Definition/ Background Of all the large carnivores, the cheetah (Acinonyx jubatus) is one of the most vulnerable to environmental degradation(Bashir et al. 2004). Kenya supports globally important cheetah populations which are currently experiencing dramatic declines in their ranges and size due to habitat loss and fragmentation (KWS 2010). Informed conservation management of the cheetah requires reliable status assessments and inferences on their ability to utilize human-influenced landscapes. Cheetah conservation activity in the country is hindered by the species’ massive area requirements and insufficient knowledge on the effects of anthropogenic activities on their population and habitat (KWS 2010).There are few quantitative studies on cheetah population status or distribution for use in conservation planning.

Previous nationwide surveys in Kenya on cheetahs were conducted to determine cheetah status and distribution after speculations that their populations were dramatically declining in the country (Isaboke et al. 2005). These surveys which provided insightful information on the species’ distribution, status population estimates and threats relied on sighting reports, limited researcher sightings and interview based surveys (Graham and Parker 1965 ,Gros 1998,Hamilton and Miller 1986). Although nationwide (large-scale) surveys that are based on questionnaires, static range maps and other forms of expert opinion as basic data provide foundational information about a species’ ecology and adaptation, they are usually inaccurate and biased because such surveys are commonly affected by the problem of species being present at some locations but going unreported. In addition, this data does not highlight the relationship between animal presence and habitat covariates.

Action for Cheetahs in Kenya (ACK) is the only range-wide cheetah conservation organization in Kenya. ACK conducted the first Kenya national cheetah survey in collaboration with Kenya Wildlife Service, Cheetah Conservation Fund and East African Wildlife Society between 2004 and 2007. Results from this survey informed national and regional strategic planning for cheetah conservation and also created the first range-wide map of cheetahs based on actual site visitation across the entire country.

In the next national survey, ArcGIS tools will be used to analyze how land cover and anthropogenic activity influence cheetah occupancy and gene flow based on field survey and genetic analysis. Information on the relationship between ecological and social determinants that influence cheetah distribution and survival will be determined on a landscape level. In addition, landscape connectivity that facilitates cheetah dispersal, especially those living in small isolated population and the genetic viability of isolated populations inside and outside protected areas across their ranges, will also be assessed.

Methods/Methodology Sampling approach for a national-wide survey A grid sampling approach will be used to sample cheetah geographical ranges in Kenya. The country will be divided into 20-km x 20-km sampling units using ArcGIS to identify the survey route. Baseline data from previous cheetah surveys in Kenya on status and distribution will be

Natural Resources Track 19/141 Noreen Mutoro, Gertrud Schaab, Mary Wykstra Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya included to identify areas where cheetah occurrence is biogeographically and ecologically possible and exclude areas where the species was historically absent.

Monitoring Cheetah distribution in Kenya Occupancy surveys will be used to determine cheetah distribution and quantify their status using presence-only data. A stratification process will be used to eliminate areas and vegetation types where the probability of cheetahs is very low (Hebblewhite et al. 2011). Fixed transect surveys will be used to detect cheetah presence/ occupancy in each grid cell. Driving transects will be conducted along the roads but in areas where there is limited road network, transects will be conducted along game trails on foot. ArcGIS Online mobile applications will be used to collect data digitally in the field and store data online to reduce the need to carry paper forms and the time in entering data.

Scat detection dogs will also be used to augment occupancy surveys by determining the proportion of landscape occupied by cheetahs based on detection of cheetah scats. Survey routes will be positioned to maximize the probability of encountering cheetah tracks both on roads, trails and at random through the study area. Detections will be represented by unambiguously identified cheetah tracks, scat or sighting reports.

A questionnaire will also be developed to gather detection/non-detection data on cheetah presence from area residents. Each respondent will be asked to report all cheetah sightings they can clearly remember and the location name and the name of surrounding landmarks. Locations where the respondent knows of the presence of the cheetah without being able to remember the precise sightings will also be recorded. For each reported sighting, the respondent will be asked to specify the date of observation or approximate dates by referring to important events in the life of the community; total number of cheetah observed and age, sex of cheetahs that were sighted. Interviewees will be ranked for confidence and results will compliment field survey and scat data.

Predicting heterogeneity in cheetah occupancy Occupancy and species distribution modeling is are an innovative methods for assessing species status, mapping species distribution and investigating determinants of species occurrence (Andresen et al. 2014). The formula developed in pilot studies based on detection and non- detection of species over several sampling occasions provide a model that can be used rapidly on larger scale .We will use aspects of distribution and occupancy methods where weighting the detection, density and search effort will provide us with the best possible formula for predicting trends in cheetah population over time. Landscape-scale occupancy surveys can also be used to identify meta-populations, which if combined with ecological (prey occurrence models) and anthropogenic information can allow the delineation of important corridors and suitable locations for reintroductions (Andresen et al. 2014).

Heterogeneity in cheetah occupancy will be determined by hierarchical ranking of predictor variables (covariates) and how they influence cheetah distribution and resource selection. A combination of environmental (climate, elevation, landscape structure and land cover/ habitat),

Natural Resources Track 20/141 Noreen Mutoro, Gertrud Schaab, Mary Wykstra Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya anthropogenic (land use, human population densities and proximity to agro-pastoralist settlement, proximity to protected areas) and biotic (occurrence probability of main prey species) spatial covariates will be used to understand cheetah occupancy across their geographical ranges (Karanth et al. 2009). A spatial vegetation community land cover model that describes vegetation community associations available in Kenya will be developed using Landsat-8/Sentinel-2 images in combination with already existing, often outdated geo-data sets. Remotely sensed data will be complemented with field based assessments on a range-wide scale to minimize errors resulting from misclassification and resolution issues, and also to put into consideration changes that may have taken place at the ground level since GIS data was collected. Geo-data on settlement, human population densities and proximity to settlements, water and protected areas will determine levels of resistance or augmentation of cheetah distribution in the area. Only covariates considered having a significant influence on cheetah distribution and habitat use in a sampling unit will be used in cheetah models.

Determining landscape connectivity GIS-based landscape layers will be combined to produce a movement cost surface quantifying the matrix between cheetah populations in terms of difficulty of movement. Least cost corridors will then be modeled across this cost surface between known cheetah populations identified in previous range wide surveys. A range-wide least cost connectivity analysis between known cheetah populations will be used to determine where potential and/or actual cheetah corridors exist. Genetic analysis from fecal samples will be used to measure the degree of maternal connections across populations and to further measure the population flow across their range. These corridors are probable connections between cheetah populations that maintain genetic viability and health in the population (Zeller et al. 2011). Field based assessments will confirm the use of the corridor by the cheetah and contribute to the remote data in ArcGIS to examine corridor boundaries for conservation planning.

Expected outcomes (Results and major findings) - Spatial distribution maps and predictive maps on cheetahs’ geographic range and status in Kenya. - Identification of habitat covariates/ resource selection functions that influence cheetah habitat occupancy in Kenya. - Identification of cheetah population patches across current geographic ranges and their genetic viability. - Identification of existing and potential corridors which will help promote corridor conservation and inform management decisions based on scientific data. - Classification of site specific threats to cheetah populations, habitats and corridors which can be used to inform management and conservation decisions.

Biological references Andresen L, K Everatt, MJ Somers. 2014. Use of site occupancy models for targeted monitoring of the cheetah. Journal of Zoology 292: 212-220.

Natural Resources Track 21/141 Noreen Mutoro, Gertrud Schaab, Mary Wykstra Improved Analysis of Cheetah - (Acinonyx jubatus) Occupancy and Gene Flow in Kenya Using GIS Tools

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Bashir S, B Daly, SM Durant, H Förster, J Grisham, L Marker et al. 2004. Global cheetah (acinonyx jubatus) monitoring workshop report. . Conservation Breeding Specialist Group (SSC/ IUCN), Pretoria. Graham AD, ISC Parker. 1965 East african wildlife society cheetah survey Nairobi. Gros PM. 1998. The status if the cheetah acinonyx jubatus in kenya: A field-interview assessment. Biological Conservation 85: 137-149. Hamilton P, SD Miller. 1986. Status of cheetah in kenya, with reference to sub-saharan africa. In: D. Everett (ed.) Cats of the world: Biology, conservation and management. Natl Wildlife Federation, Washington, D.C. Hebblewhite M, D Miquelle, A Murzin, V Aramilev, D Pikunov. 2011. Predicting potential habitat and population size for reintroduction of the far eastern leopards in the russian far east historic range of far eastern leopards. Biological Conservation, 10: 2403-2413 Isaboke W, M Kahiu, CM Wambua, M Wykstra. 2005. Cheetah census in kenya, priority 1: South western kenya (2004-2005) - report submitted to east african wildlife society, stichting- netherlands., Action for Cheetahs in Kenya. Karanth KK, JD Nichols, JE Hines, KU Karanth, NL Christensen. 2009. Patterns and determinants of mammal species occurrence in india. Journal of Applied Ecology 46: 1189-1200. KWS. 2010. Kenya national strategy for the conservation of cheetahs and wild dogs. In: Research (ed.). Kenya Wildlife Service, Nairobi. Zeller AK, S Nijihawan, R Salmon-Peréz, HS Postome, EJ Hines. 2011. Integrating occupancy modelling and interview data for corridor identification: A case study of jaguars in nicaragua. Biology Conservation: 892-901.

Biographical Notes Noreen Mutoro completed her Master’s through the University of Nairobi and Action for Cheetahs in Kenya in affiliation with the Kenya Wildlife Service and the Cheetah Conservation Fund. She is now a PhD candidate with Technische Universität München under supervision with Jany Christian Habel and Gertrud Schaab. She is a research assistant with Action for Cheetahs and works closely with Mary Wykstra, MEM, on development of the second national cheetah survey conducted with this institution

Contacts 1 Carnivores, Livelihoods and Landscapes; Acton for Cheetahs in Kenya (ACK), Nairobi, Kenya, [email protected]

2 Karlsruhe University of Applied Sciences, Karlsruhe, Germany, [email protected]

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Impact of Climate Change on Desertification in Arid Areas of Kenya

Mr. Solomon M. Mwenda, Mr. Alex Mugambi, Mr. James Nyaga Kenyatta University, Regional Centre for Mapping of Resources for Development.

Key words: Desertification, Climate Change, Arid Areas, GIS.

Abstract Climate change and a deteriorating environment is a key challenge to sustainability, bio-diversity, food security and stability across Africa. Pollution, deteriorating soil quality, desertification and poor air quality are threatening the lives and future of all the continent's people.According to previous studies, the impacts of climate change in Africa will be severe, and are already ongoing in many places. Desertification, along with climate change and the loss of biodiversity are the greatest challenges to sustainable development identified during the 1992 Rio Earth Summit. UNCCD links environment and development to sustainable land management. The Convention addresses specifically the arid, semi-arid and dry sub-humid areas, known as the dry lands, where some of the most vulnerable ecosystems and peoples can be found. UNCCD strategy (2008-2018) adopted in 2007 aims: "to forge a global partnership to reverse and prevent desertification/land degradation and to mitigate the effects of drought in affected areas in order to support poverty reduction and environmental sustainability". In pursuit of this goal, GIS modeling was used for global-aridity using the data available from the World Clim Global Climate Data as input parameters. Monthly average PET was spatially characterized and then tested using four different temperature-based methods applied to the WorldClim Global Climate Data to determine their prediction accuracy. Desertification is intensifying and spreading in Kenya, threatening millions of inhabitants’ and severely reducing productivity of the land due to a growing imbalance between population, resources, development and environment. This paper analyzed the impact of these factors on the arid areas of Kenya and mitigating measures and interventions to avert future occurrences using Geographic Information Systems.

Natural Resources Track 23/141 Solomon M. Mwenda, Alex Mugambi, James Nyaga Impact of Climate Change on Desertification in Arid Areas of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction According to a United Nations report (UNCCD, 2004) more than one billion people worldwide, most of them among the poorest in the world are affected by drought and desertification. These people, occupying approximately one quarter of the planet, are facing major problems which include soil degradation and vegetation loss, leading todeterioration of arable land and eventually to chronic food insecurity. Desertification of the arid lands of the world has been proceeding rapidly for more than a thousand years. The Arid and Semi-Arid lands (ASAL) constitute about 80% (467,200 sq. km) of Kenya’s total landmass and is grouped into geographical zones including the Savannah covering most of the North eastern and South-eastern parts, the Coastal region, the North Rift Valley, the Highlands and the Lake Victoria Basin. The ASAL hosts about 35% of Kenya’s population (13 million people) and over 60% of its inhabitants live below the poverty line, subsisting on less than one US dollar per day. (UNEP 2013).

Aridity Thompson (1975) explained that aridity and lack of moisture could be caused by climatic processes: off-shore cold currents, topography and dynamic anti-cyclonic subsidence, and high pressure systems. Desserts are found where one or more of these processes operate over a significant area for sufficient time. The arid lands are characterized by high ambient temperatures with a wide diurnal range. In most areas, evapotranspiration rates are more than twice the annual rainfall. These areas receive low anderratic bimodal rainfall that is highly variable both inspace and time. In most cases, rain falls as short highintensity storms that produce considerable runoff and soil erosion. Average annual rainfall in the arid lands ranges from 150-450mm. The soils are shallow, highly variable, and of light to medium texture. The soils are also of low fertility and are subject to compaction, capping and erosion. A few areas have volcanic soils and alluvial deposits which are suitable for crop production. Heavy clays are found in these areas also, but cultivation is difficult on them due to their poor workability as well as salinity problems. Water availability and accessibility is highly variable and is a considerable constraint to agricultural production.Arid lands are mainly inhabited by pastoralists and agro- pastoralists. Large areas are suitable only for nomadic livestock production.

Climate change. Climate change is a change in the statistical distribution of weather patterns when that change lasts for an extended period of time. Climate change may also refer to a change in average weather conditions, or in the time variation of weather around longer-term average conditions. Climate change is caused by factors such as biotic processes, variations in solar radiation received by Earth, plate tectonics, and volcanic eruptions. Certain human activities have also been identified as significant causes of recent climate change, often referred to as "global warming". Some of the general adverse effects of climate change experienced in Kenya include; Variations in weather patterns (reduced rainfall and failed seasons), frequent and prolonged droughts and diminishing water resources, Floods/flash floods and landslides, environmental degradation and habitat destruction, resurgence of pests and diseases, loss of biodiversity, severe famine and hunger causing food insecurity and resource use conflicts.

Natural Resources Track 24/141 Solomon M. Mwenda, Alex Mugambi, James Nyaga Impact of Climate Change on Desertification in Arid Areas of Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Desertification. Desertification is the impoverishment of terrestrial ecosystems under the impact of man. It is the process of deterioration in these ecosystems that can be measured by reduced productivity of desirable plants, undesirable alterations in the biomass and the diversity of the micro and macro fauna and flora, accelerated soil deterioration, and increased hazards for human occupancy. In the International Agreement on Combating Desertification that was held in Paris in 1994, desertification was defined as the reduction or loss of biological or economic productivity resulting from land use or from human activities and habitation patterns. A number of factors have increased land degradation and the vulnerability of the African arid regions to desertification. Most of them have had similar effects in Asia and Latin America. They can be grouped in three categories: Increased human and animal population, improved health services and injudicious use of technology.

Due to the increased sedentary population, pressures on cultivated land led to a shortening of the fallow period in the shifting cultivation cycle and the extension of cropping into the more precarious drier regions. Crop harvests became less reliable and more variable as the desert edge was approached. Concurrently, nomadic pastoralists were deprived of some of their best grazing lands as the cultivators moved in (Delwaulle, 1977). At the same time the rangeland area was contracting, populations of pastoralists and their livestock were increasing and the provision of improved veterinary services and the lack of a viable marketing system helped assure that animal numbers would grow rapidly (Widstrand, 1975). The result was inevitable: overgrazing and accelerated desertification. Overgrazing inadvertently was made worse, particularly in the Sahel, by the drilling of additional wells that provided drinking water for livestock throughout the year. Without the rest period that intermittent water supplies previously assured, forage conditions deteriorated around the wells where water was no longer a limiting factor in livestock survival. Local authorities did not or could not impose a control system that would allow forage plants to recover from heavy grazing. Destruction of woody vegetation has been hastened by the ever- increasing need for firewood to meet the demands of the larger population. The destruction is especially noticeable around the rapidly growing urban centers, where the circle of deforested lands gets larger every year (Delwaulle, 1973). While desertification was a long-standing problem even in the absence of droughts, the gradually increasing vulnerability of the land made the impact of the inevitable droughts worse than ever (Dahl and Hjort, 1979). The factors responsible for that vulnerability are still operating, desertification continues, and future droughts will have ever- greater damaging effects.

Droughts causes and desert spread. A common misapprehension about desertification is that it spreads from a desert core, like a ripple on a pond. Land degradation can and does occur far from any climatic desert; the presence or absence of a nearby desert has no direct relation to desertification. Desertification usually begins as a spot on the land-scape where land abuse has become excessive. From that spot, which might be around a watering point or in a cultivated field, land degradation spreads outward if the abuse continues. A second misconception is that droughts are responsible for desertification. Droughts do increase the likelihood that the rate of degradation will increase on non-irrigated land if the

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya carrying capacity is exceeded. However, well-managed land will recover from droughts with minimal adverse effects when the rains return. The deadly combination is land abuse during good periods and its continuation during periods of deficient rainfall.

Magnitude of Desertification in Africa About 18 percent of the arid region of Africa is severely desertified, and most of that represented by grazing lands and rain-fed cropping lands on the south side of the Sahara, the mountain slopes and the plains of North Africa. Moderate to high salinity affects about 30 percent of the irrigated land in Egypt (Aboukhaled et al., 1975).

Wind erosion is dominant in the drier regions and water erosion on the wetter sloping lands. Ethiopia, Kenya, and the Maghreb countries of Algeria, Morocco, and Tunisia have been subjected to especially serious water erosion, whereas wind erosion has been most damaging in sub-Saharan West Africa. While good data on the effect of land degradation on crop and livestock yields are not available, it seems likely that soil fertility losses, alone, have reduced dry land crop yields by 25 to 50 percent in the severely desertified areas. Animal productivity may well have declined by at least 50 percent nearly everywhere that domestic livestock are raised. In many areas south of the Sahara, rangeland forage production probably is less than 25 percent of the potential.

Causes of Desertification. Factors leading to desertification can in general be divided into two categories: climatic variability and human activities.

Climatic variability: Dry lands have limited water supplies (annual rainfall is less than 100mm). Rainfall can vary greatly during the year, while wider fluctuations occur over years and decades. This leads directly to drought, which is often associated with land degradation and hence a vital factor behind desertification.

Human activities: The human activities that lead to desertification can be outlined as overgrazing: This is described as the major cause of desertification worldwide and Overexploiting land: This can happen due to various reasons. It can happen due to expand in human population and hence the need for more crops, international economic forces that can lead to short-term exploitation of local resources for export.

Objectives of Study 1. To access the magnitude of desertification in Kenya’s arid areas 2. To access impact of climate change in Kenya’s arid areas 3. To analyze the impact of growing imbalance between population, resources, development and environment on the arid areas of Kenya 4. To propose mitigating measures and interventions to avert future occurrences.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Methodology Description of study site The study area in Kenya falls partly in the arid zones covering of the Rift Valley. It covers Turkana, Wajir, Mandera, Marsabit, Isiolo, Garissa, Samburu, Baringo, Tana River counties which consist of a population of 4,620,199 which is 12% of the national population. (Vision 2030 Development Strategy for Northern Kenya and other Arid Lands (2011).

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Figure 1.1: Map of Arid Areas in Kenya

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Global Potential Evapo-Transpiration (Global-PET) and Global-Aridity Geospatial Datasets were used. Potential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. The FAO introduced the definition of PET as the ET of a reference crop under optimal conditions, having the characteristics of well watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al. 1998).

The Global-PET and Global-Aridity were both modeled using the data available from the World Clim Global Climate Data (Hijmans et al. 2005) as input parameters. The World Clim, based on a high number of climate observations and SRTM topographical data, is a high-resolution global geo-database (30 arc seconds or ~ 1km at equator) of monthly average data (1950-2000) for the following climatic parameters: precipitation, mean, minimum and maximum temperature. This set of parameters is insufficient to fully parameterize physical radiation-based PET equations (i.e. the FAO-PM), though can parameterize simpler temperature-based PET equations.

Monthly average PET was spatially characterized and then tested using four different temperature- based methods applied to the WorldClim Global Climate Data to determine their prediction accuracy. The modes that were used and tested are Thornthwaite (1948), Thornthwaite modified by Holland (1978), Hargreaves et al. (1985), Hargreaves modified by Droogers and Allen (2002). The monthly average values using these high resolution temperature PET layers, together with existing medium resolution (10’) FAO-PM monthly average (1950-2000) PET layers (FAO 2004), were compared to Penman-Monteith PET values estimated at climate stations in South America and Africa (n = 2288). The PET measurements used in the validation are calculated using the more complex Penman-Monteith model applied on direct observations of the various climatic parameters, and were obtained from the FAOCLIM 2 climate station dataset (Allen et al., 1998), available online from FAO. Based on the results of the comparative validation, the Hargreaves model was chosen as the most suitable to model PET globally. (Hargreaves and Allen 2003).

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Tmean PET

Mean Annual Aridity Index Potential TD Evapotranspiration (AI)

MAP RA Mean Annual Precipitation

Hargreaves (1985) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and mean monthly extra-terrestrial radiation (RA, radiation on top of atmosphere) to calculate mean PET, as shown below: PET = 0.0023 * RA * (Tmean + 17.8) * TD0.5 (mm / day).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Average monthly and annual PET (mm) layers at spatial resolution of 30 arc-seconds (~ 1km at tropics) for the 1950-2000 period are calculated using the Hargreaves method with available layers of monthly average temperature parameters, available from WorldClim database, and extra- terrestrial radiation, calculated for specific months using a methodology presented by Allen et al., (1998). Temperature range (TD) is an effective proxy to describe the effect of cloud cover on the quantity of extra-terrestrial radiation reaching the land surface and, as such, it describes more complex physical processes with easily available climate data at high resolution. Aridity is usually expressed as a generalized function of precipitation, temperature, and potential evapotranspiration (PET). An Aridity Index (UNEP, 1997) can be used to quantify precipitation availability over atmospheric water demand.

Global mapping of mean Aridity Index from the 1950-2000 period at 30 arc second spatial resolution is calculated as: Aridity Index (AI) = MAP / MAE where: MAP = Mean Annual Precipitation MAE = Mean Annual Potential Evapotranspiration

In the Global-Aridity dataset, which uses this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. Mean annual precipitation (MAP) values were obtained from the WorldClim Global Climate Data (Hijmans et al. 2005), for years 1950-2000, while PET layers estimated on a monthly average basis by the GPET (i.e. modeled using the Hargreaves method, as described above) were aggregated to mean annual values (MAE). The Global-Aridity surface shows moisture availability for potential growth of reference vegetation excluding the impact of soil mediating water runoff events. UNEP (UNEP 1997) breaks up Aridity Index, in the traditional classification scheme presented in Table 2.

Value Climate Class < 0.03 Hyper Arid 0.03 – 0.2 Arid 0.2 – 0.5 Semi-Arid 0.5 – 0.65 Dry sub-humid > 0.65 Humid

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Results and Findings

Annual Aridity Index 1950-2009 Annual PET 1950-2009

Source: CGIAR-CSI GeoPortal (http://www.csi.cgiar.org).

Annual Average PET 1990 Annual Average PET 2000 Annual Average PET 2010

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Aridity Index 1990 Aridity Index 2000 Aridity Index 2010

Findings The magnitude of desertification in Kenya’s arid areas caused mainly by impact of climate change in Kenya’s arid areas. This has caused the growing imbalance between population, resources, development and environment on the arid areas of Kenya. There are several mitigating measures and interventions to avert future occurrences.

Interventions to Avert Future Occurrences World Day to Combat Desertification The World Day to Combat Desertification (WDCD) is observed every year since 1995 but more needs be done in order to promote public awareness on the dangers of desertification. The day could also be used as an opportunity to inform the local and international community about the implementation of the United Nations Convention to Combat Desertification in those countries experiencing serious drought and/or desertification, particularly in Africa.

United Nations Decade for Deserts and the Fight against Desertification The United Nations Decade for Deserts and the Fight against Desertification (UNDDD) is another international framework that recognizes the need to conserve and rehabilitate degraded land for enhancement of socio-economic wellbeing of the more than 2 billion dry land inhabitants worldwide. The launch of the framework on 16 August 2010 marked the beginning of a decade (2010-2020) long strategy seeking to raise awareness and action to improve the protection and management of the world’s dry land ecosystems.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Kenya National Action Programme – A framework for combating desertification Kenya ratified the United Nations Convention to Combat Desertification (UNCCD) on 24 June 1997. UNCCD, adopted on 17 June 1994, is an international legal agreement for action to combat desertification and mitigate the effect of drought in arid, semi-arid and dry sub-humid zones. One of the main commitments of the affected and developing country Parties to the Convention is to develop national action programmes (NAPs), security, and environmental conservation. Therefore Kenya national action program should: - Strengthen the knowledge base and developing information and monitoring systems for regions prone to desertification and drought, including the economic and social aspects of these ecosystems; - Combating land degradation through, inter alia, intensified soil conservation, a forestation and reforestation activities; - Developing and strengthening integrated development programmes for the eradication of poverty and promotion of alternative livelihood systems in areas prone to desertification; - Developing comprehensive anti-desertification programmes and integrating them into national development plans and national environmental planning; - Developing comprehensive drought preparedness and drought-relief schemes, such as early warning systems, for drought-prone areas and designing programmes to cope with environmental refugees; - Encouraging and promoting popular participation and environmental education, focusing on desertification control and management of the effects of climate change.

The WDCD initiative in ASALs of Kenya should support the local communities to adapt and build resilience by seeking to: - Increase food security through enhancing the drought resilience of local agricultural practices - Reduce poverty through diversification of enterprises to improve livelihoods - Facilitate the integration of adaptation to drought into Kenya’s sustainable development plans and policies - Undertake measures to reduce the vulnerability of inhabitants of ASALs to vagaries of drought - Illustrate how national policies through NAP may be influenced and modified based on lessons from the field.

Acknowledgement The authors acknowledge the support from Antonio Trabucco, The Consortium for Spatial Information (CGIAR-CSI), CEDA and Mr. John Kapoi for their immense contribution towards achieving this in providing data methodology and input of ideas and support.

References Dregne, H. E. 1986. Desertification of arid lands. In Physics of desertification, ed. F. El-Baz and M. H. A. Hassan. Dordrecht, The Netherlands: Martinus, Nijhoff. Aboukhaled, A., Arar, A., Balba. A.M., Bishay, B.G., Kadry, L.T. Rijtema, P.E., and Taher, A. (1975) Research on Crop Water Use, Salt Affected Soils and Drainage in the Arab Republic of Egypt. Near East Regional Office, FAO, Cairo, 92 p.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Albareda, J.M. (1955) Influence des changements de la vegetation dans les sols arides. In: Plant Ecology, Arid Zone Research v, UNESCO, Paris, p. 84-88. Aubreville, A. (1949) Climats, Forest, et Desertification de l'Afrique Tropicale. Societe de Editions Geographiques, Maritime et Coloniales, Paris, 255 p. Ayers, A.D., Vasquez, A., de la Rublia, J., Blasco, F., and Samplon S. (1960) Saline and Sodic Soils of Spain. Soil Science, v. 90, p. 133-138. Banco do Nordeste do Brasil (1964) O Nordeste e as Lavouras Xerofilas. Banco do Nordeste do Brasil, S.A., Departmento de Estudos Economicos do Nordeste, Fortaleza, Ceara, 238 p. Boggess, W., McGrann, J., Boehlje, M., and Heady, E.O. (1979) Farm Level Impacts of Alternative Soil Loss Control Practices. Jour. Soil and Water Conservation, v. 34, p. 177- 183. Cloudsley-Thompson, J.L. (1974) The Expanding Sahara. Environmental Conservation, 1: 5-13. Comite Peruano de Zonas Aridas (1963) Informe Nacional sobre las Zonas Aridas. Republica del Peru, Ministerio de Agricultura, Lima, 105 p. Condon, R.W. (1978) Land Tenure and Desertification in Australia's Arid Lands. Search, v. 9, p. 261-264. Cooke, R.H. and Reeves, R.W. (1976) Arroyos and Environmental Change in the American Southwest. Clarendon Press, Oxford, England, 213 p. de Crespigny, R.R.C. (1971) China. St. Martin's Press, New York, N.Y., 235 p. Dahl, G. and Hjort, A. (1979) Pastoral Change and the Role of Drought. Swedish Agency for Research Cooperation with Developing Countries Report R2: 1979, Stockholm, Sweden, 50 p. Delwaulle, J.C. (1973) Desertification de l'Afrique au sud du Sahara. Bois et Forets des Tropiques, v. 149, p. 3-20. Delwaulle, J.C. (1977) La situation forestiere dans le Sahel. Bois et Forests des Tropiques, v. 173, p. 3-22. Dougrameji, J.S. and Clor, M.A. (1977) Case Study on Desertification. Greater Mussayeb Project. Iraq. United Nations Conference on Desertification A/CONF., 74/10, 102 p. Dregne, H.E. (in press) Historical Perspective of Accelerated Erosion and Effort on World Cultivation. American Society of Agronomy, Madison, Wisconsin. General Accounting Office (1977) To Protect Tomorrow's Food Supply Soil Conservation Needs Priority Attention. Report to the Congress, CED-77-30, Washington, D.C., 59 p. Jacobsen, T. and Adams, R.M. (1958) Salt and silt in Ancient Mesopotamian agriculture. Science, v. 128, p. 1251-1258. Mabbutt, J.A. (1978) Desertification in Australia. Water Research Foundation of Australia Report No. 54, Kingsford, N.S.W., Australia 132 p. Matrinez Beltran, J. (1978) Drainage and Reclamation of Salt Affected Soils in the Bardenas area, Spain. International Institute for Land Reclamation and Improvement Publication No. 24, Wageningen, The Netherlands, 321 p. Matheson, W.E. (1978) Soil Loss Made South Australia Come Down to Earth. Journal of Soil Conservation Service of N.S.W., v. 34, p. 88-100.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Newman, J.C. and Condon, R.W. (1969) Land Use and Present Condition. In: R.O. Slayter and R.A. Perry (Editors), Arid Lands of Australia, Australian National University Press, Canberra, Australia, p. 105-132. Northcote, K.H. and Skene, J.K.M. (1972) Australian Soils with Saline and Sodic Properties. C.S.I.R.O. Soil Publication No. 27, Melbourne, Australia, 62 p. Office of Environmental Planning and Coordination (1977) Country Report: India. Department of Science and Technology, Government of India, New Delhi, 71 p. Pearse, C.K. (1971) Grazing in the Middle East: Past, Present, and Future. Journal of Range Management, v. 24, p. 13-16. Pels, S. (1978) Waterlogging and Salinization in Irrigated Semi-arid Regions of NSW. Search, v. 9, p. 273-276. Prego, A.J., Ruggiero, R.A., Alberti, F.R., and Prohaska, F.J. (1971) Stabilization of Sand Dunes in the Semiarid Argentine Pampas. In: William J. McGinnies, Bram J. Goldman, and Patricia Paylore (Editors), Food, Fiber and the Arid Lands, University of Arizona Press, Tucson, p. 369-392. Rapp, Anders (1974) A review of Desertification in Africa-- Water, Report No. 1, Stockholm, Sweden, 77 p. Soil Research Laboratory Staff (1949) Soil Moisture, Wind Erosion and Fertility of Some Canadian Prairie Soils. Department of Agriculture Publication 819, Ottawa, Canada, 78 p. Stebbing, E.P. (1937a) The Encroaching Sahara: The Threat to the West African Colonies. The Geographical Journal, 86: 506-519. Stebbing, E.P. (1937b) The Threat of the Sahara. Journal of the Royal African Society. Extra Supplement to vol. 36, 35 p., May 25, 1937. Stebbing, E.P. (1938) The Advance of the Desert. The Geographical Journal 91: 356-359. UNESCO (1977) World Distribution of Arid Regions. Map Scale: 1/25,000,000, UNESCO, Paris. United Nations (1978) United Nations Conference on Desertification. Round-up, Plan of Action, and Resolutions, United Nations, New York, 43 p. Vander Pluym, H.S.A. (1978) Extent, Causes and Control of Dryland Saline Seepage in the Northern Great Plains Region of North America. In: H.S.A. Vander Pluym (Editor), Dryland-Saline-Seep Control, Agriculture Center, Lethbridge, Alberta, v. 1, p. 48-58. Wagner, R. (1978) Soil Conservation in New South Wales, 1938-1978. Journal Soil Conservation Service of N.S.W., v. 34, p. 124-132. Weaver, J.E., and Albertson, F.W. (1940) Deterioration of Midwestern Ranges. Ecology, v. 21, p. 216-236. Widstrand, C.G. (1975) The Rationale of Nomad Economy. Ambio, v. 4, p. 146-153. United Nations Development Programme (2013) Combating Desertification in Kenya: Emerging Lessons from Empowering Local Communities. Nairobi

Contacts [email protected], [email protected], James Nyaga [email protected] or [email protected].

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya ArcGIS for Local Government

ArcGIS for Local Government includes a set of free maps, apps, and best practices developed especially for your local government. As an ArcGIS user, you can deploy this ready-to-use solution to improve government operations and enhance citizen services. A Village Level GIS-Based County Government and Environmental Risk Management Decision Support System

1*Charles Kigen and 2Philip Kisoyan and 3James Chelang’a 1,3Moi University, Department of Natural Resources, P. O. Box 3900-30100, Eldoret Email: [email protected]; [email protected] 2Egerton University, P. O. Box 536-20115, Egerton Email: [email protected] *Corresponding author

Key words: Village-level, geodatabase, decision-making, ALOHA, ArcGIS

Abstract Kenya’s devolved system of governance lead to devolution of resources and prudent decision making is key in promoting sustainable development. Resource allocation, risk and environmental management are some decisions made at the county level with limited information. This paper seeks to support the decision making process by development of a village level geodatabase containing infrastructure, and potential hazards within a given area. An area in Western Kenya was used in the study, data was sourced from google earth and threat levels from chlorine gas pollution modeled using ALOHA software. The geodatabase was constructed and spatial analyses run in ArcGIS. These processes were digitization, buffering, distance generation, distance extraction, intersection and land size estimation. The results generated the number of households and their proximity to roads, schools, piped water networks, rivers and electricity grid. Further, the number of households and size of land under different pollution threat levels were generated. The integration of this information in the decision making process is invaluable in guiding infrastructure development, classifying the population and land under different levels of threats by chlorine gas pollution and eases the process of identification of affected individuals and land.

Local Government Track 38/141 Charles Kigen, Philip Kisoyan, James Chelang’a A Village Level GIS-Based County Government and Environmental Risk Management Decision Support System

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Decision making is a process of making assessments and conclusions based on certain factors under consideration among them being limited resources and hazards. The achievement of sustainable development goals (SDGs) and Kenya’s Vision 2030 is dependent on prudent decision making. Decisions have been made in many cases using political support leading to uneven development. Judicious decisions are made if data and the factors to be taken into consideration are given attention. The advent of Kenya’s decentralized system of governance presented many uncertainties in carrying out development projects. The county governments have implemented many projects and established risk management plans for certain emergencies. However, the projects and prioritization can be improved by collecting baseline information which constitutes population distribution, infrastructure and potential hazards. This paper examines the potentials of application of GIS technology to aid decision making at village-level about project prioritization in terms of available resources but also about the siting and also for emergency planning and management.

The GIS spatial analysis requires establishment of a geodatabase containing the entire infrastructure, the potential hazard and their locations. An area in western Kenya, part of Webuye town was used in the study. The area was picked due to the presence of a pulp paper factor and the study assumed a scenario where chlorine gas escaped accidentally. The data of interest in the area included road networks, piped water network, power lines, public schools and the potential risks of accidental release of chlorine gas modeled using Areal Locations of Hazardous Atmospheres (ALOHA). ALOHA was used in the Chlorine gas emission modeling because of its compatibility with ArGIS (Chakraborty and Armstrong, 1994). ALOHA is a free software developed by United States Environment Protection Agency (EPA) and National Oceanic and Atmospheric Administration (NOAA). It contains about 1000 database of chemicals that can be released into the atmosphere through tank explosion, leaking pipes and open containers (EPA and NOAA, 2007). It predicts the direction of these chemicals based on the prevailing weather conditions which are required to generate the direction and concentrations of any given chemical at a given point in the area of interest. The ALOHA’s use in the modeling is because it enables chemical plumes footprints to be exported into ArcGIS platform ((EPA and NOAA, 2007); Chakraborty and Armstrong, 1994) for integration with other data and further analysis.

Spatial analysis of the data was done against the population distribution of the area. The results showed the population distribution of the area, the proximity of each household to the infrastructure and river network, the location and number of households, the size of land under the different chlorine gas acute exposure guideline levels (AEGLs). Moreover, the population to be compensated and the size of land to be rehabilitated can be quickly and easily estimated. With such information and powerful spatial analysis tools, a decision support system can be established at a village-level to foster even sustainable development for a given area using the open source software.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Methodology Area of study The area of study lies between 692000m and 69900m longitude and 60000 and 65000m latitude in zone 36N (figure 1). The area population is high with an average density of 515 people per square kilometer. The regions poverty index is one of the highest in the country at 57%. The major economic activities are small-scale subsistence farming, cash crop farming (sugarcane), businesses and employment. The major water sources in the village of study comprise springs/rivers and piped network mostly in the urban areas.

Geodatabase Construction The household, public schools, river network and road network data were extracted from google earth maps while the power lines and water networks were hypothetical. ArcGIS software was used in personal goedatabase construction and spatial analysis. The google earth maps were downloaded, assigned WGS 1984 Projection and registered using the latitude and longitude grids in decimal degrees before digitization of the required data. For all the data, distance raster were generated using ‘euclidean distance’ tool (figure 2) and then distance of each household extracted using ‘extract multi value to points’ tool following guidelines from Darbra, et al. (2008). The generated distance table was categorized at intervals of 500m and the number of households in each group.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 1: The villages of study close to the Pan paper pulp factory

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 2: The distance raster and distribution of households (a), schools (b), power lines (c), roads (d), piped water (e) and rivers (f)

Chlorine gas plume footprint modeling and analysis Modeling chlorine gas footprint in ALOHA required data (table 1) which include site data (location, building air exchanges rate and time), chemical data (chemical name, molecular weight, AEGL for 60mins, (Immediately Dangerous to Life and Health Limits) IDLH, ambient boiling point, vapor pressure at ambient temperature and ambient saturation concentration), atmospheric data (wind velocity and direction, ground roughness, cloud cover, air temperature, inversion height and relative humidity) (EPA and NOAA, 2007). Other useful information required to run ALOHA model is source of chlorine gas strength that comprise source, flammability, tank diameter, tank Length, tank volume, other materials in the tank, internal Temperature, chemical mass in tank, internal press, opening length, opening width, release duration, max average sustained release rate, (averaged over a minute or more) and the total amount released.

Table 1: The data required to run the ALOHA model SITE DATA: Location: WEBUYE, KENYA Building Air Exchanges Per Hour: 0.28 (unsheltered single storied) Time: July 17, 2016 0253 hours ST (user specified)

CHEMICAL DATA: Chemical Name: CHLORINE

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya CAS Number: 7782-50-5 Molecular Weight: 70.91 g/mol AEGL-1 (60 min): 0.5 ppm AEGL-2 (60 min): 2 ppm AEGL-3 (60 min): 20 ppm IDLH: 10 ppm Ambient Boiling Point: -39.3° C Vapor Pressure at Ambient Temperature: greater than 1 atm Ambient Saturation Concentration: 1,000,000 ppm or 100.0%

ATMOSPHERIC DATA: (MANUAL INPUT OF DATA) Wind: 2.24 miles/hour from 45° true at 10 meters Ground Roughness: open country Cloud Cover: 3 tenths Air Temperature: 20° C Stability Class: F No Inversion Height Relative Humidity: 90%

SOURCE STRENGTH: Leak from hole in horizontal cylindrical tank Non-flammable chemical is escaping from tank Tank Diameter: 2.5 meters Tank Length: 7 meters Tank Volume: 34.4 cubic meters Tank contains gas only Internal Temperature: 20° C Chemical Mass in Tank: 513 kilograms Internal Press: 70 psia Opening Length: 90 centimeters Opening Width: 6 centimeters Release Duration: 1 minute Max Average Sustained Release Rate: 6.95 kilograms/sec (averaged over a minute or more) Total Amount Released: 417 kilograms

The output of chlorine gas plume model are a text summary table (table 1) containing all the input data used chlorine concentration threat zones of Red (1.6 kilometers with a concentration of 20 ppm), Orange (4.1 kilometers with a concentration of 2 ppm) and Yellow (7.2 kilometers with a concentration of 0.5 ppm) (figure 3). Generated also are the threat levels wind direction confidence lines. The chlorine gas plume was the exported to .kml format and converted to shapefiles in ArcGIS while maintaining the threat levels and the wind direction confidence lines (figure 4). In the ArcGIS, the number of households, schools and land under different levels of threats of Chlorine gas were extracted and tabulated as modified from Jakala (2007).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 3: Chlorine gas plume ALOHA footprint

Figure 4: Exported ALOHA chlorine gas plume to ArcGIS

Results and Discussion Household Proximity to infrastructure A total of 5355 households were in the study village distributed as in figure 2 (a). The household are concentrated more in the northwest section a peri-urban area and decrease away low concentration in the other areas. The village infrastructure distribution and coverage is not uniform

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya (figure 2 (b), (c), (d) and (e)) while the river network is as figure 2 (f). The distances of households away from the infrastructure and rivers were measure in meters at intervals of 500m table 2. The schools distribution is fairly even with 45.2% of households within a distance of 500-100m and 0.5% at 2500-3000m away. While the power lines cover more of the urban areas with over 80% of the households within 500m, 1.1% is 1500-2000m away. The all-weather roads though not tarmac showed the same pattern as power lines with 80.9% of the households within 500m. The piped water network covers a small section mainly in the urban and western areas of the village. Almost 50% of the households are within 500m, with 2.9% at 2500-3000m away from the water network. The natural rivers and the stream networks cover almost the entire village where over 90% of the population is at 0-1000m away. The furthest households from the river were 0.3% at a distance of less than 2000m.

The distance raster of the infrastructure away from the household give indication of not only what infrastructure to prioritize but also where to locate it. Figure 2 (b, c, d, e and f) show infrastructure coverage and in case the government wants to expand the infrastructure, consulting such spatial data add value in the decision making process.

Table 2: Percent distribution of infrastructure and rivers Distance (km) Schools Power line Roads network Piped water River

500 24.5 81.0 80.9 48.7 55.4

1000 45.2 17.9 17.5 22.4 35.9

1500 17.0 1.1 1.6 11.6 8.3

2000 10.0 - - 6.6 0.3

2500 2.8 - - 5.4 -

3000 0.5 - - 2.9 -

Environmental Risk Analysis and Management Further, the spatial data analysis has invaluable benefits in the environmental risk analysis and management. The study hypothesized an accidental release of chlorine gas from the nearby pulp paper industry. The chlorine gas plume is a function of weather elements and it spread in the village is from northeast to southwest direction (figure 4). The plume concentration was categorized into three threat zones each with a wind direction confidence (table 3) (EPA and NOAA, 2007).

Table 3: Chlorine gas plume threat levels Threat zone Percent Population Schools Area (km2) Major crops households Red (over 20ppm) 1.6 1336 1 0.78 Sugarcane Red wind direction confidence 5.5 2347 0 1.37 Sugarcane

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Orange (over 2ppm) 6.8 1825 1 2.18 Sugarcane Orange wind direction confidence 21.4 4683 2 8.23 Sugarcane Yellow (over 0.5ppm) 8.9 1258 3 3.82 Sugarcane Yellow wind direction confidence 46.8 8403 7 22.65 Sugarcane

The results of chlorine gas plume spatial analysis were in relation to the number of households, population, schools, area of land and the major crops at risk and the levels of threats. The threat levels were three namely red (over 20 ppm), orange (over 2 ppm) and yellow (over 0.5 ppm) each with wind direction confidence lines. The red threat zone affects 1.6% of the households with a population of 1336 individuals, 1 (one) school and covers an area of 0.78 km2 with major crop being sugarcane. This threat level requires more attention as the population, land and crops will be exposed to the highest levels of chlorine where maximum negative impacts will be experienced. The red wind direction confidence line takes care of wind uncertainties that can change direction locally due to barriers such as hills and tall trees. The household percent population in this zone was 5.5 with a population of 2347 covering an area of 1.37 km2. The yellow threat zone with the least concentration of chlorine gas at less than 0.5 ppm covered the biggest area of 3.82 km2 with a population of 1258 individuals. The area being in Kenya’s sugar belt zone, sugarcane is the major crop grown and will be negatively impacted.

The availability of such information is valuable in the environmental risk analysis and management processes. The identification and estimation of the affected households and population as shown is key in planning for emergencies. The hospitals within the vicinity should be equipped to deal identified risks and the capacity of affected populations by each threat level. Those in the red and orange threat level zones should be given priority when it comes to medical attention. In case of the need for evacuation, the authorities should also be in the know of the schools affected and populations to be able to put up an effective reaction plan. Compensation for victims can be estimated using the same spatial information. Since the population is under the different threat levels is known, the total compensation for the victims can be easily calculated.

The generated data of this nature (table 3) can also be used to identify and quantify the size of land affected and the type of crops damaged by the chlorine gas precipitation from the atmosphere. In the study village the soils will require remedial actions such as application of base elements to counter soil acidity. Once the change in soil characteristics are established, it is possible to quantify the required amount of base elements using the size of land affect in each threat zone. This information which can be generated within a short time is valuable to risk and environmental management managers as they aid response plan to the emergency at hand as also concluded by Tseng (2012).

The GIS potentials in Kenya have not been realized and establishment of village level godatabase and its objective application has the capacity to overhaul decision making processes more so in the County governments. The requirements for the decision making support system are a GIS

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya platform, air pollution modeling software which are freely available for use and skilled person in GIS.

Conclusion and Recommendations Spatial planners and environmental risk and management planners are yet to realize the benefits of using Geographic Information Systems for visualization of information and as an important planning tool. The establishment and use of a geodatabase in combination air pollution modeling software will be an invaluable resource for planning and responding air pollution scenarios. The tools can also be used in impact analysis stage in the environmental impact assessment process and guide on the expected scope of the impacts. However, there are issues with model validation that needs to be addressed especially in hilly areas and when the wind velocity is very low. The study has demonstrated usefulness of a village level goedatabse and recommends its establishment by the county governments.

Reference Chakraborty, J. and Armstrong, M. 1994. Estimating the Population Characteristics of Areas Affected by Hazardous Materials Accidents. GIS- LIS. Pp. 154-163. Retrieved February 10, 2007 from EBSCO database. Darbra, R.M., Demichela, M. and Murè, S., (2008). Preliminary risk assessment of ecotoxic substances accidental releases in major risk installations through fuzzy logic. Process Saf. Environ. Prot., 86, pp. 103-111. Environmental Protection Agency and National Oceanic and Atmospheric Administration (2007) ALOHA User’s Manual. Washington D.C. Seattle, WA. Jakala, S. D. (2007). A GIS Enabled Air Dispersion Modeling Tool for Emergency Management. Volume 9, Papers in Resource Analysis. 20pp. Saint Mary’s University of Minnesota Central Services Press. Winona, MN. Retrieved on 4th September 2016 from http:/www.gis.smumn.edu Tseng, J.M., T.S. Su, C.Y. Kuo (2012) Consequence Evaluation of Toxic Chemical Releases by ALOHA. International Symposium on Safety Science and Technology Procedia Engineering 2012, Vol.45:384–389, doi:10.1016/j.proeng.2012.08.175,

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste Disposal Site: A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania

Mike Yedgo, Tanzania

Abstract The study uses GIS based modeling in assessing pollution vulnerability of groundwater in the vicinity of solid waste disposal sites. Heavy Metal contamination of the groundwater inside and around the Pugu Kinyamwezi dumpsite was assessed; pollution modeling using shallow water wells, and populations at risk were assessed, as was future water demand. Water from about 12 shallow wells was taken for testing. According to the WHO, the Maximum Contaminant Levels (MCL) for Copper, Lead, chromium Zinc and Cadmium were 1.5, 0.01, 0.08, 3 and 0.003mg/L respectively.

Results obtained from the laboratory were used as a benchmark in finalizing the model. Pollution modeling was carried out through the construction of a water table contour map. In this case, a shallow well was measured in height using ropes during morning hours, instead of digging a shallow well to reach the point of water seep. Therefore measurements were taken before any disturbance of the shallow well. In modeling of spatial distribution of pollutants in the case study, a water table contour, which is useful in predicting groundwater flow, was used ARCGIS 9.3 in developing water table contour. The output flow direction indicated groundwater flows were east and south east of the case study area. Leachate distribution/movement underground was successfully presented using ArchiCAD 15.

The model developed paves the way for effective action before conditions worsen. Continuous consumption of shallow well water for drinking purposes results in waterborne related health problems and other problems due to heavy metal contamination.

Heavy saturation of the soil in the disposal site would also increase transportation of groundwater pollutants. Bacterial contribution, present in the moisture in the disposal site, would increase leachate production that would be transported by the groundwater in the modeled site.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Dar es Salaam city consists of three municipal authorities: Temeke, Ilala and Kinondoni. Each disposes of waste at the Pugu Kinyamwezi dumping site. Observation shows that the equipment used includes truck loaders, tractors and others.

In Dar es Salaam today, groundwater has become one source of water supply because the government is unable to meet the ever increasing water demand. Thus, inhabitants have had to look for alternative groundwater sources such as shallow wells and boreholes. The quality of these groundwater sources is affected by the characteristics of the media through which the water passes on its way to the groundwater zone of saturation; thus, the heavy metals discharged by industries, traffic, municipal wastes, hazardous waste sites, as well as from fertilizers for agricultural purposes and accidental oil spillages from tankers, can result in a steady rise in contamination of ground water (Igwilo et al., 2006).

Open dumping and non-engineered disposal sites in Pugu ward portray negative attitude for the population in the area. Leachate development and seepage to groundwater is inevitable in the site, which experiences average annual rainfall of 1300 mm. Moreover, groundwater movement is inevitable, thus actual transportation of pollutants is also inevitable in the soil and groundwater. Emphasis should be placed on raising awareness to prevent people from contracting illnesses associated with contaminated water. The quality of water is affected by the quality of groundwater entering the system of water supply in the borehole (Shwille, 2000). This is because the water table elevation is approximately the same as the gaining borehole surface elevation; both elevations may be used to construct water table maps (contour) and to predict groundwater flow direction.

Mapping and Site Characterizations of Existing Situation Location Pugu Kinyamwezi dumpsite, located in Pugu ward, Ilala Municipality (see Figure 1), is the current main dumping site for most solid wastes in Dar es Salaam city. The rapid population increase is influenced by both natural causes and immigration (birth rates and net immigration rates respectively). Pugu ward has an estimated population of 49,422, of which 24,159 are males and 25,263 are females. The Average Household Size is 4.2, according to the national census of 2012.

Description of Pugu dumpsite Pugu ward experiences a modified type of equatorial climate. It is generally hot and humid throughout the year, with an average temperature of 28oC. The hottest season is from October to March, while it is relatively cool between May and August.

Currently, the Pugu dumpsite operates as an open dumpsite, receiving waste from different areas of Dar es Salaam city, namely, Temeke, Ilala and Kinondoni municipalities, with extreme lack of: designed cells; full leachate management; full landfill gas management; daily soil cover; a final soil cover and a compaction process; a fence with a gate; daily record of volume, type, and source of waste; and a waste scavenging plan.

The dumpsite is approximately 20 km from the city centre and lies at latitude 6° 51' 41" S and longitude 39° 07' 02" E. It covers an area of approximately 75 hectares. The site was previously used as a sand quarry and then afterward turned into waste disposal site, which has been in operation since 2007 after the closure of another dumpsite, namely Mtoni.( ERC, 2004).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

The site receives solid wastes containing both industrial, agricultural, domestic, commercial, institutional, medical and other special wastes (yard wastes, batteries and electronic). Since the site indicates no control of waste moisture content, waste decomposition in the site is inevitable (Valencia et al. 2009). The temperature is around 23ºC. There are two rain seasons: the short rains from October to December and the long rain season between March and May. The average annual rainfall is 1300 mm.

Water supply The source of water supply in Pugu ward is groundwater – shallow wells, deep wells (boreholes), and both individual and government owned water wells. Fresh water from shallow wells observed in the case study is used for drinking purposes and for other domestic use.

Social services Social services include education, safe and clean water, health (health centers both private and public owned by individuals) and energy distribution (different sources of energy, such as electricity, kerosene, charcoal, firewood, solar, etc.), transportation infrastructure such as main roads, railways, etc.

Existing land use patterns Land units in Pugu ward hamlets are characterized by weathered slopes and are well drained with unconsolidated clay-bond sands. An occasional outcrop of raised coral limestone also occurs inthe area. Furthermore the dumpsite is characterized by formation of leachate that seeps in the soil. This also plays a significant role in altering the geographical condition of the surrounding area for residents of Pugu.

Economic Activities Trade and agriculture are the most important economic activities in the area. Trade is mainly limited to small scale petty traders in the informal sectors of the ward’s population. Small shops and market stands are a common sight and ensure the distribution of consumer goods to all the sub-villages in the area.

Hydrological conditions The uppermost water-bearing unit in the study area is the unconfined aquifer, which consists primarily of unconsolidated materials. The unconfined aquifer is shallow in the south-western part with an average thickness of 10 m and deep in the eastern part of the study area, with a thickness of up to 50 m. The lower aquifer system is under semi-confined conditions in unconsolidated sediments. It is considered as a Pleistocene to Recent deposit and has an average thickness of 100 m. The semi-confined aquifer overlays the base of the groundwater reservoir, formed by an aquifer of a thickness of about 1000 m in the Mio- Pliocene clay-bound sands. The figure below presents the features of the hydrological condition of Pugu areas (Mjemah, 2012b).

The compacted layers of aquitards (clay, silt or rock) in the case study play significant roles in retarding water flow underground; that is, they act as a barrier for groundwater - they form a body of material with very low permeability. Aquitards separate aquifers and partially disconnect the flow of water underground. The space is mainly dominated by a seasonal water table. During the dry season, some of the existing shallow wells become dry, between 10 and 15 m deep. Deep water in the Pugu ward exists at not less than 80 m. The water at this depth is mainly dominated by saline and is used for domestic purposes such as

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya drinking during seasons of water scarcity. Pugu ward satisfies its water-supply demands almost entirely from the freshwater contained within the unconsolidated sediments of Pleistocene.

Solid waste existing practice Solid waste entrance in the site It has been estimated by the authorities that approximately 4,200 tons per day of solid waste were generated in Dar es Salaam as per 2011. This represents a generation rate of 0.93 kg/cap/day based on a population of 4.5 million (Robert, 2012). The authorities have also estimated that less than 40% of the total wastes generated in the city are collected and disposed of in the Pugu dump site or otherwise recovered. The remaining wastes (approximately 60%) are either dumped by the road side or into drainage canals, contributing to health problems for local residents, annual flooding events and methane generation (Robert, 2012).

Waste category compositions Waste composition entering the dump site contains food waste, paper, textiles, plastic, grass/wood, metal, glass and other materials. This indicates accumulation of solid waste in the site that ensures high generation of various parameters (heavy metal, physical-chemical and biological).

Water usage The residents near the disposal site use groundwater, both shallow and deep well. They also use seasonal water for their domestic use during periods of rainfall. With extreme development and the increase in population, the need for water is growing, and in the future the heavy deterioration of groundwater quality may be high. Excessive groundwater withdrawal has the severe effect of lowering the water table in some well fields. As a result, contamination via water pumping, especially on this site, is inevitable, as well difficult to detect. This can be done only through monitoring of excessive pumping of the nearby well. Dumping sites for municipal waste often produce leachate that migrates to adjacent areas, resulting in gross pollution of soil, surface water and groundwater. The leachate may contain matter that is resistant to biological or chemical changes, and that therefore remains in the soil for many years. This study aims at mapping the site characterization, assessing available water quality, both in shallow wells and boreholes, developing and modeling spatial pollution distribution at the site, and an assessment of risk exposure due to use of the water well in the modeled site, as well as general water demand.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure: 1 Pugu dumpsite location Material and Method This section provides information on materials, tools and methods used, including a literature review, laboratory analysis, physical observation of the case study area, a questionnaire, consultation, and process based computer GIS software version 9.3 and ArchiCAD 15 based software, in developing a model of underground leachate movement. Materials used include: plastic bottle (1 litre), nitric acid ropes, AAS tape measure.

Samples were taken from selected water wells, samples for cations analysis were acidified with nitric acid to around pH=1.5. Prior to the sampling process and transportation the sample was preserved using Nitric acid in a sample digestion process. 1 ml of Nitric acid was added to each 1-litre bottle of sample water, and the bottle was finally stored in a freezing container at 4°C. The samples were analyzed within 4 hours of sampling. No filtration was done because the samples were assumed not to interfere with the AAS i.e. any presence of organic impurities was disregarded.

A shallow well near the dumpsite was chosen in the development of the model, based on the nature of geological and hydrological conditions of the area.

Result and Discussions

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Table 1 below depicted the results. Sampling location Parameter concentration (mg/L) n=3 (AAS)

Cu Pb Cr Zn Cd WW-1 0.00817 0.98067 0.16033 0.238 0.0115 WW-2 0.01333 0.201 0.11533 0.06867 0.02333 WW-3 0.021 1.10433 bal 0.05767 0.03767 WW-4 0.01067 0.92567 0.022 0.08267 0.02733 WW-5 0.02667 Bdl bdl 0.15667 0.02667 WW-6 0.0105 0.24933 bdl 0.52333 0.02717 WW-7 0.011267 0.162 1.131333 0.151333 0.0346 WW-8 0.0024 0.572 0.3523 0.0583 0.534 WW-9 0.0079 Bdl 0.0641 0.0701 bdl WW -10 bdl 0.084 0.256 0.13 0.068 WW-11 bdl 0.021 bdl 0.004 0.002 WW-12 0.02 0.255 0.393 bdl 0.051 TBS 2 0.2 1 5 0.013 WHO 1.5 0.05 0.08 3 0.05

Pugu Dump Site Pollution Modeling, Risk Analysis and Water Demand Pollution modeling Modeling is the process of producing a model; a model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents, and is useful in prediction of the effect of changes to the system.

The aim of this model is to investigate the effectiveness of the groundwater in carrying pollutants from the disposal site to inhabitant areas. Modeling the spatial distribution of the pollutant in the regional area of the case study has significant impact on decision making for present and future generations. Leachate that contains different toxic parameters such as heavy metal, volatile organic compounds and others has significant impact on deterioration of groundwater. These parameters are potentially generated at the disposal site.

Groundwater flow predictions Water contours, which predict the flow of groundwater, have been used in this research to show the groundwater flow pattern; using water well data from a survey of 50 water wells within the case study areas, a water table contour map was constructed. The water table level was assumed to be the same as that of the water well top level. The water table elevation in the case study area is well described in the map, and this marks the point that plume dispersion of the pollutant may be back and forth in movement. Thus, it can be concluded that a water table range of 8.36 – 9.45 ft and 7.2 – 8.36 ft may have higher vulnerability to pollutants from the disposal site.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Modeling analysis and assumptions A large portion of the areas surrounding the disposal site was found to be dominated by sand soil. Different studies indicate that the average groundwater flow in sand soil is 0.24384 to 0.70104 m/day. Based on analysis performed on heavy metal in the case study, with reference to the operational phase of the disposal site (2007- 2014), maximum concentration of the water well in the sampling point was a result of 7 years. Modeling of pollutants for the next seven years (2014-2021) was important to knowing the safeness of the water for the future generation. Future distance dispersion was computed using an average flow velocity of 0.24384 m/day for groundwater in sand soil.

The estimated rate of ground-water flow through the sand and gravel is 0.8 to 2.3 ft/d. This estimate was obtained from Darcy’s law (LeBlanc and Guswa, 1977) V= K(dh/dl) / n Where v = average velocity, K = hydraulic conductivity, dh/dl = hydraulic gradient (change in water-table altitude with distance), and n = effective porosity Using the values of hydraulic conductivity and the water-table slope given above, and assuming an effective porosity of 0.20 to 0.40 for sand and gravel, V= (200 to 300 ft/d)(8 ft/5280 ft) = 0.8 to 2 .3 ft/d . 0.20 to 0.40 The average velocity of groundwater in the fine to very fine sand and silt, and the sandy till is lower than the velocity in the sand and gravel because the hydraulic conductivity of the fine-grained sediments is much lower than the hydraulic conductivity of the sand and gravel.

Modeling development, assumption and Output Under certain environmental conditions, disposal sites are susceptible to pollutant dispersion in many ways, as the result of extreme continuous decomposition of solid waste. At the Pugu Kinyamwezi disposal site, which is an open and uncontrolled dumping system, different receptors such as air, surface water, soil and groundwater are exoposed to various output products. Interaction with both surface water and ground water results in deterioration of groundwater quality, in hand with water exploitation through water pumping. Modeling made different factors constant in order to successfully display pollutant migration in the groundwater in the sand soil, which was found to be the most significant in posing environmental problems. An estimation of pollutant migration from Pugu dumpsite towards residential areas was developed under the following assumptions

 Initial concentration of pollutants in the case study before disposal of solid waste disposal site was assumed to be below detection limit  Only the advection process will take place in this model (groundwater will be moving relative with the pollutant from the disposal site), and other factors are minor  Laboratory analysis performed from various sampling points in the case study was used as the initial concentration for developing model table 4.1  Water and pollutant movement in the subsurface behave with the same properties of flow, with an average velocity of 0.24384 m/day for sand soil, which is the predominant type in the case study.  The existence of a very low steep slope, with relative uniform structure in the region, comprised of sand soil, while the layer beneath the sand soil was assumed to contain material of aquitard which does not easily allow water flow. Thus down seepage of leachate in the area was disregarded.

Local Government Track 54/141 Mike Yedgo GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste Disposal Site: A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya  The assumed control point in the disposal site was used as the reference control point. Flow velocity was used to compute the distance from the disposal site, to identify the affected residential areas.  The shallow well was given more weight in developing the model.  Darcy's law on the average flow velocity of groundwater is 0.8 -2.3 ft/day for sand and gravel soil.

Output of the model Without any engineered and environmental scientific application in the Pugu disposal site, contamination will increase to a very large extent. A large portion of the population will be exposed to heavy metal contaminants from the disposal site.

Seven years of Pugu dumpsite operation could contain a maximum pollution concentration that can move underground, polluting other wells.

Heavy metal, which is invisible, was assumed to move with the water and be transferred to various areas outside the disposal site. Using ArchCAD version 15 as a powerful tool, the modeled output was designed.. Analysis of the shallow well, with its distance from the assumed disposal site, has been shown clearly in figure 2 and 3.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Figure: 2 Contour and water flow descriptions in the case study

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Figure 3: Water well used as benchmark for model development

Figures 4 and 5 below harness the existing situation of figure 3 above, presented with ArchiCAD graphic software. Figure 4 is an indication or output of seven years of dumpsite operation. Using initial output in the first model, 7 years of projection, using average flow distance, indicated that the flow of groundwater contaminated with the pollutant from disposal site in sand soil will move a distance of 695.5 m from all initial sampled points used as initial values in developing this model. Figure 6.5 indicates the extension of the initial model forecasted to 7 years using Darcy's flow formula. The distance deduced has been used in

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya a map to verify the most affected areas in figure 3, such as Majohe ward and Kinyamwezi suburb.

Figure: 4 Lead projection distance of contaminated well for seven years of dumpsite operations

Figure: 5 Modeled lead concentration projection for 7years

Sensitivity of the model Continuous consumption of shallow well water for drinking purposes will result in health problems such as waterborne diseases, and others, where there is no boiling or other form of pre-treatment for heavy metals. Heavy saturation of the soil in the disposal site would also yield higher values of groundwater pollutant transportation. Bacterial contribution as the presence of moisture in the disposal site would increase leachate production that will eventually be transported by groundwater in the modeled site.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Growth rates in the development of new pollutants will affect the groundwater quality. The model did not take this into account, but the distribution of pollutants in the future will resemble the heavy metal concentration obtained with 7 years of operation.

It assumed that the modeled site will be affected by the same pollution towards the end of a projected point of10 years.

Conclusion and Recommendations Conclusion A GIS based simulation model was successfully designed through characterization and mapping of the existing situation, analysis of groundwater samples. GIS simulation modeling through water table contour mapping played a greater role in prediction of groundwater flow, hence successfully showing the flow direction of pollutants from the disposal site to different existing residential habitats. Mapping and characterization of the existing situation, through field surveys, observation and other methods, showed that the dug wells are contained in unconfined aquifers, which are mainly sand soil. During the rain period the water table rises. On the other hand, the wells are more vulnerable to groundwater contamination since the nature of the soil is sand soil, which is easily saturated, hence allowing for easy movement of water contaminants. The water table contour map revealed that the direction of groundwater flow was toward the eastern and south eastern part of the dump site. Our analysis indicated that the people of Majohe, Kinyamwezi, in the eastern part, are susceptible and vulnerable to heavy metal pollutants from the disposal site.

Recommendations Disposal practice in Pugu Kinyamwezi does not reflect a scientific well planned sanitary landfill. It is therefore recommended that dumpsites should be modified in order to accommodate the desirable standard for better management of solid waste. Based on the flow pattern of the aquifer system in the case study, groundwater contamination in resident water wells is inevitable, hence leading to human exposure to heavy metal as the result of continuous use of groundwater for domestic purposes.

Based on the findings and analysis of the results presented in this paper, and the model developed, it is concluded that water from water sources around the eastern side of the dumpsite is not safe for drinking. Thus it is recommended that dumpsites should be modified in order to accommodate the desirable standard for better management of solid waste.

 Transforming disposal i.e. well-designed landfills with provision of capping, lining; effective arrangement of the site for evaporation and leachate treatment could reduce pollutant migration/movement from the disposal site via groundwater.  Capping of the site and gas capturing from the disposal site could also reduce volatile organic and inorganic components, some of which are suspected to cause cancer in case of inhalation. Dermal contact of solid waste and distribution of heavy metal would be minimized at large scale.

Reference Adelekan B.A.; (2010), International Journal of Water Resources and Environmental Engineering, 2 (6): 137-147. Adepoju-Bello, A.A. and O.M. Alabi, 2005. Heavy metals: A review. The Nig. J. Pharm., 37: 41-45.

Local Government Track 59/141 Mike Yedgo GIS Based Modeling to Assess Pollution Vulnerability to Groundwater in the Vicinity of Solid Waste Disposal Site: A Case Study of Pugu Kinyamwezi Dumpsite in Dar Es Salaam, Tanzania

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Bakare-Odunola M. T., (2005) Determination of some metallic impurities present in soft drinks marketed in Nigeria. Nig. J. Of Pharm. Res.4 (1) 51-54. Berti, W. R., and D. Jacobs, 1998. Distribution of trace elements in soil from repeated sewage sludge applications. J. Environ. Qual. 27:1280–1286 Borgmann, U. (1983). Metal Speciation and Toxicity of Free Metal Ions to Aquatic Biota. In: Nriagu J.O. (ed.) Aquatic Toxicity. Advances in Environmental Science and Technology. Vol. 13 John Wiley & Sons. New York. Pp. 47 – 73. Conant, B., Cherry, J. A., and Gillham, R. W.: A PCE groundwater plume discharging to a river: 10 influence of the streambed and near-river zone on contaminant distributions, J. Contam. Hydrol., 73(1–4), 249–279, 2004. Davis, A.P., Shokouhian, M., Sharma, H., Minami, C. and Winogradoff, D., (2003). Water quality improvement through bioretention: Lead, copper, and zinc removal. Water Environment Research, 75(1): 73-82. ECDG. 2002. European Commission DG ENV. E3 Project ENV. E.3/ETU/0058. Heavy metals in waste. Final report. ElingeCM.;Itodo AU.;Birni-Yauri UA.; and Mbongo A.N.;(2011) Advances in Applied ScienceResearch, 2(4): 279-282. Freeze R.A. and Cherry J.A. (2002). “Groundwater” Prentice-Hall, Englewood cliffs New Jersey, PP 604. Igwilo, I.O., O.J. Afonne, U.J. Maduabuchi and O.E. Orisakwe, 2006. Toxicological study of the Anam River in Otuocha, Anambra State, Nigeria. Arch. Environ. Occup. Health, 61(5): 205-208. Johansen, P., Muir, D., Asmund, G., Riget, F., 2004. Human exposure to contaminants in the traditional Greenland diet. Science of the Total Environment 331, 189e206. Mjemah, I.C Mtoni, Y., Bakundukize, C., Van Camp, M., Martens, K. and Walraevens, K.(2012b). Saltwater intrusion and nitrate pollution in the coastal aquifer of Dar esSalaam,Tanzania. Springer, Environmental Earth Sciences (DOI: 10.1007/s12665-012-2197-7). Oliver N.M and Ismaila Y;.(2011)Advances in Applied Science Research, , 2(3): 191-197 Oman C. and Rosqvist H., (1999) Transport fate of organic compounds with water through landfills. Water Research, 33, 2247– 2254. Oseji, J. O; Asokhia, M. B and Okolie, E. C. (2006): “Determination of Groundwater Potential in Obiaruku and Environs Using Surface Geoelectric Sounding”. The Environmentalist, Springer Science + Business Media, DO1 10.10669-006-0159-x Vol. 26 Pp (301 – 308), Netherlands. Pellerin C, Booker S.M (2000) Reflections on hexavalent chromium. Environ Health Persp 108:402–407 Rao K J and Shantaram M .V. (2003) Workshop on sustainable land fill management, channel, India, , 27- 27. Shwille, F. (2000). “Groundwater Pollution in Porous Media by Fluids Immiscible with water” Quality of Groundwater, Proceedings of an International Symposium. Langley R.B. “Why is the GPS Signal so Complex” GPS world Vol. 1 No. 3, May/June PP. 56-59 U.S. EPA. (2002c). A Review of the Reference Dose and Reference Concentration Processes. Risk Assessment Forum, Washington, DC, EPA/630/P- 02/002F.http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=55365 US EPA, (2005) Guidelines for Carcinogen Risk Assessment Risk Assessment Forum U.S. Environmental Protection Agency; EPA /630/P-03/001BMarch 2005 Washington, DC US EPA, 1986 Guidelines for Carcinogen Risk Assessment Published on September 24, Federal Register 51(185):33992-34003 WHO, (2004) Guidelines for Drinking Water Quality. 3rd Edn.Vol. 1 Recommendation, Geneva, 2004, 515.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya World Health Organization, (2006): National water quality guidelines for domestic consumption. Zietz, B.P., J. Lap and R. Suchenwirth, (2007). Assessment and management of tap water Lead contamination in Lower Saxon, Germany. Int. J. Environ. Health Res., 17(6): 407-418.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Comparing two geospatial approaches for delineating crop ecologies in Tanzania

Francis Kamau, MUTHONI, Tanzania International Institute of Tropical Agriculture (IITA)

Key words: Arcgisbinding package, Extrapolation detection tool, Improved varieties, Fertilizers, Spatial targeting

Abstract Mapping distribution of suitable environmental conditions for agronomic technologies guides spatial targeting to enhance adoption. This paper compares a bottom-up and a top-down geospatial approach for delineating suitable ecologies for a technology package comprising of improved maize varieties and fertilizers. Bioclimatic variables for the Feed the Future zone in Tanzania were utilised. Maize yields data from trial sites were used to identify the variety and fertilizer treatment with the best performance. For top-down approach, GIS overlay operations were conducted to delineate suitable zone for best performing technology. For bottom-up approach, the extrapolation detection tool was used to generate maps on two types of dissimilarities between the bioclimatic conditions at reference site and outlying projection domain and a map of the most limiting variable. SC719 maize variety grown with YaramilaCereal and Sulfan fertilizers was the best performing treatment in trials. The top-down approach delineated 15% of FtF zone as suitable. The bottom-up approach revealed the magnitude of deviation from univariate range and novel combinations of environmental covariates between the reference site and the projection domain. Precipitation was most limiting factor. Although the top-down approach is more insightful, its applicability in Africa is limited by sparse crop trial data.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Food insecurity is a prevalent problem in sub-Sahara Africa and the situation is worsened by increasing population. Adoption of improved crop varieties that are high yielding and tolerant to drought, pests and diseases is one panacea to increase food production. These varieties are disseminated together with related good agronomic practices (GAPs) as technology packages to increase yields and conservation of natural resource capital [1]. The potential impact and the rates of adoption of these agronomic practices can be accentuated if they are disseminated in their suitable biophysical environments [2]. To a large extent the (dis)similarity in environmental conditions is a proxy for differences in crop suitability [3]. Therefore delineation of zones with similar environments will guide spatial targeting of agronomic technologies to areas with the highest potential. Geographical Information Systems (GIS) and remote sensing tools are used to delineate suitability indices at landscape, regional and global scales [4].

This paper compares the utility of a top-down and bottom-up geospatial approaches for mapping suitability of integrated agronomic technologies in the Feed the Future (FtF) zone in Tanzania. The paper evaluates the suitability of improved maize varieties treated with different fertilizers. Input variables include selected gridded biophysical layers that are known to limit crop growth and efficiency of inorganic fertilizers. Results demonstrate the differences between the two approaches followed by a discussion on advantages and the context in which each method is most suitable.

Methods Study area The study area covers the FtF zone in Tanzania (Figure 1). Figure 2 summarizes the implemented workflow for comparing a top-down and bottom-up approaches for delineating suitable zones for scaling integrated agronomic practices comprising of improved maize varieties and inorganic fertilizers. The environmental layers used in both methods are shown in table 2.

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Figure 1. The location of demo sites where improved maize varieties and application of inorganic fertilizers was evaluated in Mbozi District, Mbeya region in Tanzania. The inset map to the right show the extent of FtF zone that used as the projection domain.

Statistical analysis Study area and the data The study was conducted in the Feed the Future (FtF) zone that covers 594,282 Km2 area (31.9oE, -3.4oS; 38.5oE, -10.6oS). The study area is within five administrative regions in Tanzania (Figure 1). The FtF zone is used as the projection domain since the aim is to extrapolate the successful agronomic technology packages to suitable biophysical environments within this zone. Demonstration plots for improved maize varieties and application of fertilizers were implemented during the 2016 growing season in 16 farms located in four administrative wards in Mbozi Distirct, Mbeya region (Figure 1). Although the sites were primarily intended to demonstrate the best-bet technologies to farmers, they are hereafter referred to as trial sites since different treatments combining maize varieties and inorganic fertilizers were evaluated. A polygon of four wards (783 Km2) where trials were conducted was used as the reference site.

Three bioclimatic grid layers (Table 2) with 1 Km resolution were obtained from the Worldclim database [5]. The elevation layer in metres above sea level was obtained from 30m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Elevation Model Version 2 [GDEM-V2; 6]. The elevation layer was resampled to 1 Km resolution. The four layers (Table 2) were selected based on variety release reports and expert knowledge of maize breeders that pointed to their relevance in determining the suitability of maize varieties.

Table 1. Characteristics of maize varieties and fertilizers treatments evaluated in plot trials in Mbozi District.

Optimal biophysical range Attainable Grain Altitud Rainfal Maturit Treatment Variety yield (t/ha) e (a.s.l.) l (mm) y (days) Fertilizer type ID DAP+ Urea Ver1Fer1 800- YaramilaCereal + HB 614 7 >1500 1500 180-190 Sulfan Ver1Fer2 DAP+ Urea Ver2Fer1 MERU 800- 700- YaramilaCereal + 513 11 1200 1500 100-110 Sulfan Ver2Fer2 DAP+ Urea Ver3Fer1 PAN 800- YaramilaCereal + 691 7 >1500 1500 103 Sulfan Ver3Fer2 DAP+ Urea Ver4Fer1 800- 800- YaramilaCereal + SC 719 4.5-5.0 1500 1200 145-153 Sulfan Ver4Fer2 DAP+ Urea Ver5Fer1 1200- 800- YaramilaCereal + UH 615 8.0-9.0 1800 1200 85-92 Sulfan Ver5Fer2 DAP+ Urea Ver6Fer1 UH 1200- 800- YaramilaCereal + 6303 9.0-10.0 1800 1500 92 Sulfan Ver6Fer2

Table 2. The geospatial layers used in analysis. All grid layers were resampled to 1Km resolution. The letter z in bracket was used to distinguish the layers for the reference area in Mbozi District from that of entire FtF zone.

Code Variable name Resolution Source DEM(z)ftf Elevation 1 Km ASTER DEM [6] pptcv(z)ftf Precipitation seasonality 1 Km Bioclim [5] (coefficient of variation) ppt(z)ftf Annual precipitation 1 Km “ Tempcv(z)ftf Temperature seasonality 1 Km “

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Data exploration and analysis Analysis was conducted using Arcmap 10.4.1 and R for statistical computing [7]. Processing and analysis interchanged between Arcmap 10.4.1 and R particularly using the newly released ‘arcgisbinding’ package [8]. Other R packages utilized intensively includes the ‘raster’ [9] and ‘BiodiversityR’ packages [10]. Boxplot plots for maize yields recorded in different treatments were generated using ‘BiodiversityR’ and utilised to select the best performing treatment. For top-bottom approach, the DEM and precipitation layers were used to run GIS overlay operations to delineate suitable zones based on expert knowledge on optimal limits of the candidate technologies (Figure 2). The two layers for seasonality could not be used since it was not possible to obtain information on thresholds for the candidate maize variety. The suitable area for the best performing maize variety was delineated from each grid layer based on optimal thresholds for growth of candidate maize varieties (Table 1). The resulting suitability maps based on individual layers were intersected to generate the final binary map on suitability map for candindate variety.

For bottom-up approach, the extrapolation detection (ExeDet) tool [11] was used to calculate an environmental dissimilarity surface between the reference site and the projection domain (FtF zone). ExeDet is multivariate statistical tool that use Mahalanobis distance to measure the dissimilarity between a reference site and a projection domain by accounting for both the deviation from the mean and the correlation between variables. The projection domain represents the search region that is targeted for extrapolation of candidate technology package. The method return maps on two sources of dissimilarity (novelty); the novel univariate range (NT1) and the novel combinations of covariates (NT2). NT1 map shows the magnitude at which the environmental conditions at any particular location in the projection domains fall outside the range of values observed in the reference sites [11]. NT1 ranges from zero to an infinite negative value with zero indicating no extrapolation beyond the univariate coverage of reference data. The lower the value is from zero the more the environmental dissimilarity of a location compared to the conditions in the reference site.

The NT2 map identifies locations where individual covariates are within the ranges observed in the reference site but the combination of observed values of covariates are different (differing correlation). The value of NT2 range from zero up-to an infinite positive value. Values ranging from 0 to 1 indicate similarity in terms of both univariate range and multivariate combination, with values closer to zero being more similar [11]. Locations within this range (0 to 1) is very similar to reference sites and therefore is the most suitable for scaling the target technology.Values larger than one are indicative of novel combinations of environmental variables. Moreover the Exedet tool [11] generates a map of the most influential covariate (MIC), showing the environmental variable that is most limiting the suitability of a technology in every pixel the projection domain. The MIC is determined by considering both NT1 and NT2. The MIC map identifies where any particular covariate has the most extreme univariate ranges (NT1) or its highest contribution to the largest correlation distortion (NT2).

Although there is a standalone ExDet tool [11], for this analysis we recoded the ExeDet algorithm in R and plotted the NT1 and NT2 maps using BiodiversityR package. Two sets of input grid layers

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya (Table 2) were clipped to the extent of the reference site and the projection domain. The ExeDet calculated the dissimilarity between environmental conditions in the reference site (area of 4 wards in Mbozi District) and the projection domain that encompass the entire FtF zone (Figure 1). The returned NT1 map showed a gradient of increasing dissimilarity from range of values of individual covariates that were observed in the reference sites. Sites with the lowest dissimilarity to the reference sites (less negative values) are designated as potentially suitable for scaling the maize variety and fertilizer technology package that performed well in the reference site. The MIC maps identify the biophysical variables that induce the highest limit to suitability of particular technology package in different locations of the projection domain.

Figure 2. Flow chart summarizing the workflow for comparing the top-down and bottom-up geospatial approaches for identifying extrapolation domains for scaling maize varieties and fertilizers.

Results Selecting the best performing integrated technology Results from trial experiment revealed that treatment Ver4Fer1 that comprise of SC719 improved maize variety and application of Yaramila-Cereal and Sulfan fertilizers had the highest grain yields (6.2 t/ha; Figure 3). Therefore this was selected as the best-bet technology package in the trials conducted in Mbozi District. The bottom-up and top-down geospatial approaches were explored for identification of suitable locations for extrapolating this best-bet technology package in the entire FtF zone in Tanzania (sections 3.2-3.3).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 3. The grain yields recorded from the experimental treatments with 6 different improved maize varieties and 2 fertilizers in Mbozi district. Varieties treated with fertilizer 2 had higher grain yields except for variety 6 (UH 6303). Treatment Ver4Fer1 recorded the highest yield (6.2 t/ha).

Top-down approach Results obtained after GIS overlay operations revealed that an area covering 156,817 Km2 in the Ftf zone had suitable altitudinal range for growth of SC719 maize variety (Figure 4). However after intersecting with the area with suitable precipitation range, only 89,782 Km2 (15% of FtF zone) was finally earmarked as suitable (Figure 4).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 4. Biophysical suitability of technology package comprising SC719 maize variety and Yaramila-Cereal and Sulfan fertilizers in the FtF zone in Tanzania derived by overlaying the optimal elevation and precipitation range. The thresholds for optimal range are indicated in table 1.

Bottom-up approach A comparison of grid layers representing the reference site and projection domain revealed that the later had higher variance compared to the former (Figure 5). The NT1 Map revealed the gradients of dissimilarity in univatiate values falling outside the range observed in reference data (Figure 6). The dissimilarity ranges from 0 to -12 (increasing dissimilarity gradient from purple to red tone). Values close to zero are relatively similar to the reference site and therefore interpreted as suitable to scaling-out the technology package comprising of SC719 maize variety with application of Yaramila-Cereal and Sulfan fertilizers. The section with value -12 is extremely dissimilar and therefore highly unsuitable for the candidate technology.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya The NT2 map ((Figure 6) reveals the magnitude of occurence of novel combinations of covariates. Locations with values ranging 0-1 are the most similar to the reference sites and should be the first priority in scaling-out the best-bet technology. The MIC map reveals that annual precipitation was the most limiting factor in the largest area (Western) followed by precititation seasonality (Eastern) and temperature seasonality (South-East) (Figure 7).

Figure 5. Boxplots reflecting the variation of environmental conditions for (a) the reference site in Mbozi District and (b) the projection domains covering the entire FtF zone. The range of values of biophysical variables observed in the reference site are a narrower subset of values in projection domain. The elevation (demftf) of the reference site ranged from 1000 - 1700 but in the projection domain it ranged between 100-3000 m A.S.L.

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Figure 6. Map for NT1 and NT2 novelties generated by the ExeDet tool from the 4 gridded biophysical layers. NT1 represents the magnitude by which values of individual covariates fall outside the range observed in the reference site. The NT1 values range from zero to -12. The reference site is the white polygon at the extreme South West in the NT1 map. Region immediately surrounding the reference site have NT1 values less than 1, therefore they are more similar to the reference site. Negative NT1 values reflect the degree of environmental dissimilarity between the reference site and the projection domain, hence the decreasing suitability of candidate technology. The NT2 map shows the degree at which the environmental conditions in the projection domain exhibit novel combinations of covariates. NT2 ranges from zero up to infinite positive values. Values ranging from 0 to 1 indicate

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya similarity in terms of both univariate range and multivariate combination and therefore this is the most suitable locations for scaling candidate technology. Values larger than one are indicative of novel combinations.

Figure 7. Map of the most important covariate (MIC) in determining the suitability of SC- 719 maize variety and fertilizer treatment in the FtF zone in Tanzania. MIC is the covariate that is most limiting the suitability of candidate technologies. The suitability of SC719 maize variety with application of Yaramila-Cereal and Sulfan fertilizers in in Eastern, Western and Southern section of the FtF zone was largely influenced by precipitation seasonality (pptcvzftf), annual precipitation (pptzftf) and temperature seasonality (tempcvzftf) respectively. The area shaded blue (labelled 'none' in the legend), is the reference site and therefore does not have any covariate with values outside the range nor any non-analogous combination of covariates. The codes for variables are listed in Table 2.

Discussion Differences in approaches This paper compares two geospatial approaches for delianating suitability maps for integrated agronomic technology package comprising of improved maize varieties and inorganic fertilizers. Results revealed that the utility of the two approaches is dependent on the context in which they are applied. The top-down method utilizes gridded geospatial layers on biophysical environment to delineate suitability maps based on known biophysical range of optimal performance of particular technology package. This approach is most suited for locations where crop trial data is sparse but with considerably high expert knowledge on biophysical requirements of candidate technologies. Although the top-down approach is widely used [3, 12, 13], it is overly simplistic since it evaluates a single variable at a time without factoring the possibility of unique

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya combinations of environmental variables across sites that may significantly affect suitability of a particular crop variety [11].

The bottom-up approach is suitable for localities with long-term crop trials data. Information obtained from long-term trials are utilized to identify the combinations of agronomic technologies with the best results based on selected attributes such as crop yields or stover biomass. The long- term trials increase confidence in identification of agronomic technologies with the best perfomance at particular locations. These locations are consequently used as reference sites when deriving the extrapolation domains. Results obtained from the bottom-up approach is more intuitive beacause the ExeDet tool utilise all the the input environmental layers to generate the dissimilaity gradients compared to the GIS overlays that use each layer at a time. The NT2 dissimilarity is generated by considering unique combinations of values rather than a binary approach in the top-down approach that only check if the values are within the optimal range or not. Some locations delianated as suitable by the top-down method solely because values of individual covariates layers were within the suitable range for particular technology can be earmarked to be unsuitable if the combinations of the environmental variables are different.

Relevance The two approaches facilitate identification of suitability gradients of integrated agronomic technology packages that guide extension agencies and development programs when selecting sites for scaling-out. The higher variance of environmental conditions in the projection domain compared to the search domain highlights the fact that scaling of agronomic technologies has significant uncertainty. This emanated from extrapolation beyond the environmental space observed in trial sites since performance of varieties in the novel conditions in unknown. The results generated by ExeDet tool showing the magnitude of deviation from the univariate range of environment in the trial sites would assist extension agents in reducing risks associated with the failure of a technology when introduced in unsuitable environments. Moreover the map showing the spatial distribution of the most limiting factor for particular technology across the projection domain would be useful for extension agencies when recommending remedial solutions for increasing yields at different locations. For example, irrigation schemes could be recommended in locations where low precipitation is the most limiting factor. The suitability maps are also important to crop breeder’s interested in establishing multi-locational trials to develop cultivars with specific environmental adaptation [4].

Limitations In this study, the trials to identify the best performing technology package were conducted in one growing season. The confidence in the performance of particular technology could be enhanced by using long-term experimental trials, expert knowledge on performance of candidate agronomic technologies in different agro-ecologies and simulation models. Moreover only biophysical variables are included in the analysis although suitability of crop varieties is influenced by variety of factors such access to market and consumer preferences. Incorporating these socio-economic variables in the analysis is limited by availability of reliable spatial data at appropriate scale.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya References Vanlauwe, B., et al., Sustainable intensification and the African smallholder farmer. Current Opinion in Environmental Sustainability, 2014. 8: p. 15-22. Jolly, C.M., The use of action variables in determining recommendation domains: Grouping senegalese farmers for research and extension. Agricultural Administration and Extension, 1988. 30(4): p. 253-267. Nijbroek, R.P. and S.J. Andelman, Regional suitability for agricultural intensification: a spatial analysis of the Southern Agricultural Growth Corridor of Tanzania. International Journal of Agricultural Sustainability, 2015: p. 1-17. Hyman, G., D. Hodson, and P. Jones, Spatial analysis to support geographic targeting of genetypes to environments. Frontiers in Physiology, 2013. 4. Hijmans, R.J., et al., Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 2005. 25(15): p. 1965-1978. METI and NASA. ASTER Global Digital Elevation Model (ASTER GDEM) version 2. 2011 [cited 2015 29/10]; Available from: http://www.jspacesystems.or.jp/ersdac/GDEM/E/4.html. R Core Team. R: A language and environment for statistical computing. 2016 [cited 2015 10/29]; Available from: https://www.R-project.org/. ESRI Arcgisbinding package. 2016. 16. Hijmans, R.J. Raster: Geographic Data Analysis and Modeling R package version 2.5-2 2015 [cited 2015 10/10]; Available from: https://CRAN.R-project.org/package=raster. Kindt, R. and R. Coe, Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. Vol. ISBN 92-9059-179-X. 2005, Nairobi: World Agroforestry Centre (ICRAF), Nairobi. Mesgaran, M.B., R.D. Cousens, and B.L. Webber, Here be dragons: A tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions, 2014. 20(10): p. 1147-1159. Notenbaert, A., et al., Identifying recommendation domains for targeting dual-purpose maize-based interventions in crop-livestock systems in East Africa. Land Use Policy, 2013. 30(1): p. 834-846. Tesfaye, K., et al., Identifying Potential Recommendation Domains for Conservation Agriculture in Ethiopia, Kenya, and Malawi. Environmental Management, 2015. 55(2): p. 330-346.

Contacts Dr. Francis Kamau Muthoni Postdoctoral Fellow – GIS Specialist - Africa RISING Project International Institute of Tropical Agriculture (IITA) c/o, The World Vegetable Center, P.O. Box 10, Duluti, Arusha, TANZANIA Tel: +255 272 553051 | Mobile no: +255 785252 986 Email: [email protected] www.iita.org or http://africa-rising.wikispaces.com/

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-Central Nigeria

Mohammed Abdulkadir and Amina Yusuf Department of Geography and Faculty of Natural Sciences,Ibrahim Badamasi Babangida University, Lapai, Niger State, Nigeria.

KEY WORDS: Urban Growth, Land use/ change and Agricultural

Abstract The study explores the urban growth analysis on agricultural landuse in Kuta and its environs. The objectives of the research is to examine the implication of urban growth encroachment on agricultural land, between 1990, 2001 and 2013 respectively. Land-sat imageries of Kuta and its environs for 1990, 2001 and 2013 were acquired, processed and classified using GIS techniques. The methods adopted in this research were maximum likelihood classification and area calculation in hectares for the various land use/land cover for each study year. The result shows that, in the year 1990 the built-up area was 1481.662 hectares and farmland was 36165.98 hectares, this indicates that urban area was very small and agricultural activity was at the peak in 1990. In the year 2001, the built-up area increased to 2584.641 hectares and agricultural land decreased to 20323.35 hectares. This indicates that urban growth was gradually taking place at the expense of agricultural land. In the year 2013, built-up area increased to 10074.373 hectares and agricultural land decreased to 16530.98 hectares. This shows that there was a significant change from 2001 to 2013 as urban area grows four times of its size, diminishing agricultural land. Result also shows that between 1990 and 2013, the rate of urban growth encroachment on agricultural land was 9.61%. It was found out that urban growth has more negative effects than positive effects on agricultural land. It was recommended that Government by way of policy should be strict in preserving farmland from illegal occupation, in order to reduce the monitor reduction of farmlands by human activities in Kuta and its environs, Shiroro Local Government of Niger State, Nigeria.

Local Government Track 75/141 Mohammed Abdulkadir and Amina Yusuf Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-Central Nigeria

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Urban growth had become a global phenomenon affecting all countries of the world, rich or poor. This phenomenon became a challenge to most countries of the world, especially developing ones. The high rates of population increases and consequently, the depletion of farmlands around rural, and urban areas. In recent years, understanding the dynamics of urban growth, quantifying them and subsequently predicting the same for a future period has attracted significant interest of research. Growth of urban centers consumes farmland. This has resulted in low food productivity (Groot et al. 2002, Curran and de Sherbinin 2004)

Nigeria being a developing nation has most of it population dwelling in rural areas, this is because they mainly engage in primary activities, and their dependences much on the natural environment for their livelihood. Among these primary activities, is agriculture, which has being the most vital and predominant activity. The rapid increase in population has placed great demands on the available living space. As the population increases, there is need to provide residence and infrastructural facilities (i.e. road, water supply, electricity, sewerage and drainage) for the entire populace, leading to the conversion of agricultural land to build-up areas at an incredible rate and decreasing the size of lands for agriculture (Nuissl et al. (2008).

Urban growth has been criticized for eliminating agricultural lands, spoiling water quality, and causing air pollution (Allen and Lu, 2003). As population increases, so does the need for new housing, schools, transportation and other civic amenities increases at the expense of agricultural land (Wilson et al 2003).

The technologies of Geographical Information Systems (GIS) and Remote Sensing have been combined to detect changes in urban growth and project the rate of urban encroachment in a way which is easier and faster than the traditional methods of surveying the urban environment. In this study, 1990, 2001and 2013years period land-use changes in Kuta and its environs were examined. To access the effects of urban growth on agricultural land in Kuta town and its environs and analyze change detection on Farmland using multi-temporal Remote Sensing data and GIS based techniques, to identify Land use land cover (LULC. The main LULC types identified were the bare ground, built-up, water body, vegetation and farmland.

Study Area Kuta is the Headquarter of Shiroro Local Government of Niger State. The study area lies between latitude 9°50̍25̎and 10°04̍18̎ North and longitude 6°51̍16̎ and 6°82̍82̎ East, on a geological base of undifferentiated basement complex of mainly gneiss and magnetite. It is bounded by Gurmana and Erena to the north, Zumba (Shiroro hydro-electric power station) to the northeast, Shata to the southwest and Gijuwa to the west respectively.

It is located in a tropical climatic zone, experiencing distinct wet and dry season with annual rainfall varying from between 110mm and 1600mm. The rainfall characteristics of the study area are from March to Octobe. In April, rainfall is at 70mm or more, covering the central part of the

Local Government Track 76/141 Mohammed Abdulkadir and Amina Yusuf Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-Central Nigeria

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya local government, and the peak value is about 280-300mm from July to October. It determines the weather condition of the area, encourages agriculture due to its influence on vegetation.

The study area has an average temperature of 13.88°c (67°f), while it is about 34.44°c (94°f) between March and June.

The topography is highly undulating and varied in height. Isolated hills of over 600m above sea level are common, while the valley in-between can get as lower as 500m above sea level. The natural vegetation is guinea savanna type, characterized with tall grasses and scattered trees. The grasses are between 1.5 to 3.5m height, the trees are short, bold broad leaves of up to 16.5m in height, riparian and gallery forest are predominantly along the river valley. The soils are derived from the Precambrian basement complex rocks, comprising of granite, gneiss and amphiboles.

Figure1.1: Nigeria showing Niger State Source: Researcher’s work

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 1.2: Niger State showing Shiroro LGA Source: Niger state Ministry of Land and Survey

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Figure 1.3: Land Use Map of Kuta and its environs Source: Niger State Urban and Town Planning

Research Methodology Data Acquisition

Table 1.1: Land-sat characteristic Landsat Data used Acquisition Date Dimensions Actual Spatial Acquisition for the study (in Pixels) Resolution Source ETM+ 17/12/1990 7327 x 7757 30 m x 30m GLCF ETM+ 2001 8525 x 7512 30m x 30m GLCF NigSat X 2013 7586 x8707 22m x 22m NARSDA

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Image Geometric Correction This is to correct for geometric errors associated with the satellite images due a variety of reasons. These include instrumental errors, attitude of the sensors with respect to the rotation of the Earth and swath width of the sensor etc. Also image registration is executed to assign coordinates systems and projections to images. Image registration ensures that the features and process found on the satellite image are allocated to their correct dimensions and positions on the ground location. This is very important for change detection since we only compare the same geographic location at different times. The image registration in this study was executed with the Arc-GIS software from ESRI. The images were registered to the 1984 World Geodetic System Universal Transverse Mercator (WGS ’84 UTM) in the Geographic Coordinate System.

Although the images were already geo-referenced to the Geographic Coordinate System, they were re-projected to UTM ‘84 Zone 32 N so as to ensure that they are allocated their correct ground coordinates. This is normally referred to as geometric correction or raster projection. The satellite image was imported into Arc-GIS 10.1 for data conversion from tiff format to .img format to be used in Erdas Imagine 9.2 software for analysis.

Data Analysis. Table 1.0 below: Shows that, in 1990, urban area was 1481.662 hectares, (1.09%). While agricultural land was 36165.98 hectares, (26.62%). This implies that agricultural activity was at very high rate and urban activity was very low in 1990. However, by 2001 comparative analysis of 1990 and 2001 images, show little changes in the land use, as urban area increased to 2584.641 hectares, (1.903%). While agricultural land diminished to 20323.35 hectare, (14.95%). This implies that urbanization was gradually coming up as of 2001

Table 1.0: Magnitude and Annual Change rate of land use between 1990 and 2001 Land use A B C D E type 1990 2001 Magnitude of Annual rate % change Area in Area in (B-A) change C/A *100 hectares hectares C/12 Farmland 36165.98 203233.5 -15842.63 -1320.23 -43.81% 67.67% Bare surface 41628.54 69798.11 28169.57 2347.46

Built up Area 1481.662 2584.641 1102.979 91.91 74.44%

Vegetation 54561.86 40876.22 -13685.64 -1140.47 -25.08%

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Water bodies 2013.55 2269.263 255.713 21.309 12.699%

Figure 1.0: Land use classified image of 1990 and 2001

Table 2.0 below: Shows that in 2001, urban area increased from 2584.641 hectares, (1.903%) in 2013 to 10074.373 hectare, (7.415%) and Farm land decreases to 16530.98 hectares, (12.17%). This indicates that Kuta and its environs experienced rapid urban growth within the period of 2001 and 2013, as it increased to four times of what it was in 2001. Consequently, farm land and vegetation were decreased to two times of their sizes

Table 2: Magnitude and Annual Change rate of land use between 2001 and 2013 Land A B C D E use/cover types Area in Area in Magnitude of Annual rate of % change hectares hectares (B/A) chances C/A * 100

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya 2001 2013

Farm land 20323.35 16530.98 -3792.37 -316.03 -18.66%

Bare surface 69298.11 77436.26 7638.1 636.51 10.94%

Built up Area 2584.641 10074.373 7489.732 624.14 289.78%

Vegetation 40876.22 29496.6 -11379.62 -948.30 -27.84%

Water bodies 2269.263 2313.42 44.157 3.68 1.95%

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Figure 2.0.: Extent of Land use image classification between 2001 and 2013

Table 3 below: Shows that in the year 1990, water body covered an area of 2013.55 hectares, (1.482%); built-up area covered an area of 1481.662 hectares, (1.090%); farmland covered an area of 36165.98 hectares, (26.62%); bare ground covered an area of 41628.54 hectares, (30.46%) and vegetation covered an area of 54561.86 hectares, (40.16%).In the year 2001, water body increased to 2269.263 hectares, (1.67%); built-up area increased to 2584.641 hectares, (1.903%); farmland decreased to 20323.35 hectares, (14.95%); bare ground increased to 69798.11 hectares, (51.37%) and vegetation also decreased to 40876.22 hectares, (30.08%). While in the year 2013, water body increased from what it was in 2001 to 2313.42 hectares, (1.702%); built-up area drastically increased to 10074.37 hectares, (7.415%); farmland decreased to 16530.98 hectares, (12.17%); bare ground increased to 77436.21 hectares, (57%) and finally the vegetation decreased to 29496.6 hectares, (21.71%) as illustrated in 3. Table 3: Magnitude and Annual Change rate of land use between 1990 and 2013 Area Area Area (%) Land use/cover (Hectare) (%) ( Hectare ) (%) ( Hectare ) Types 1990 2001 2013 Water body 2013.55 1.482 2269.263 1.670 2313.42 1.702 Built up area 1481.662 1.090 2584.641 1.903 10074.373 7.415 Farmland 36165.98 26.62 20323.35 14.95 16530.98 12.17 Bare ground 41628.54 30.64 69798.11 51.37 77436.21 57.00 Vegetation 54561.86 40.16 40876.22 30.08 29496.6 21.71 TOTAL AREA 135851.582 100 135851.582 100 135851.582 100

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Table 3.0: Land use analysis between 1990 and 2013 Conclusion Urban growth on agricultural land in Kuta town and its environs and change detection analysis on Farmland using multi-temporal Remote Sensing data and GIS based techniques, indicates that farm land has been widely consumed, or encroached. The trend of farmland, forest and vegetation cover loss within the study area could be explained by the LULC conversions to residential purposes and the lumbering activities in the area. This loss is attributed to the built-up areas, between 1990 and 2013. Using Satellite image data, GIS and RS technique can be a valuable tool in locating and predicting Farm land and forest cover change. Thematic maps of forest cover types and various LULC classes can be distinguished by the satellite image interpretations and to evaluate their conversions as well as analyzing their trends. These aids in farmland and forest cover change detection and identification of areas under risk of invasions. Finally, the study area is experiencing a lot of socio-economic and political changes that is impacting negatively on the ecological landscape. In the area of farmland sector as assessed in this research, the following observations are noteworthy: i. Urbanization is taking its toll on Kuta and its environs faster than envisaged

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya ii. The urbanization growth rate of the area is increasing significantly from 1.090 to 7.415 hectare between l990 to 2013. iii. A total of 19635 Ha of farmlands had been lost to urban growth within 23 years period under study, which has led to food insecurity.

References Allen, J., Lu K. 2003. Modeling and Prediction of Future Urban Growth in the Charleston Region of South Carolina: GIS-based Integrated Approach. Conservation Ecology 8 (2): 2. Batty M. (2008), The size, scale and shape of cities, Science 319-771. Bhatta, Basudeb, 2010, Analysis of urban growth and sprawl from sensing data. Springer Curran S. R. and De Sherbinin A. (2004), Completing the Picture: The Challenges of Bringing “Consumption” into Population, Environment Equation, Population and Environment, 26 (2): 107-131. De Groot R. S., Wilson M. A. and Boumans R. M. J. (2002), A typology for the classification, description and valuation of ecosystem functions, goods and services, Ecological Economics, 41 (3): 393-408. Nelson AC (1999). Analysis based on indicators with policy implications. Land Use Policy, 16: 121-127. Wilson, E.H., Hurd, J.D., Civco, D.L., Prisloe, M.P., Arnold, C. 2003. Development of a geospatial model to quantify describe and map urban growth. Remote Sensing of Environment 86: 275-285.

Contacts Email:[email protected] Email: [email protected]

Local Government Track 85/141 Mohammed Abdulkadir and Amina Yusuf Analysis of Urban Growth and Agricultural Land Use of Kuta in Shiroro L.G.A, Niger State, North-Central Nigeria

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya National Government

Enhancing quality of life is the goal. Proven location-based technology is the key to achieving it.

A Geographic information System driven integrated land management System

Mary Wandia, Kenya National Lands Commission

Key words: GIS, NLIMS, workflows, integrated system

Abstract The National Land Commission is currently developing a National Land Information Management System (NLIMS). This is a parcel-centric based Geographic Information System (GIS) solution geared towards automating land processes and procedures. The system is to be implemented using a five-phase strategy. So far, phase 1 has been implemented with the setting up of infrastructure, system design and development, integration of GIS with other systems and automation of land processes which include land administration, valuation, settlement and adjudication. Once finalized, the system will support all components in land administration using an integrated approach. The integrated system is based on Microsoft SQL server, Dynamics NAV, SharePoint and ArcGIS solutions. In retrospect, it will enhance provision of land management services and provide a platform for citizen interaction through an online portal featuring free services and for pay services.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction The National Land Commission has been mandated to manage public land on behalf of the national & county governments as outlined in the National Land Commission Act, 2012 Section 5 (2) (d) (GoK, 2009: Gunk, 2012). This implementation shall be at both national and county level. Currently, the Commission is in the process of implementing a National Land Information Management System (NLIMS) to streamline, harmonize and improve service delivery to stakeholders in land matters and the general public. Theoretically, NLIMS is a system that comprises of sub-systems to support processes of the components of land administration1 in Kenya (See Error! Reference source not found. below). In this context, system efinition comprises (i) software, (ii) data, (iii) workflows (procedures & processes), (iv) network infrastructure and (v) staff. The system is designed to create, process, analyze and publish parcel-based data such as parcel information, location, zoning, land-use, ownership and any other general property information.

Figure 2 Components of land administration and management in Kenya

Prior to the establishment of the Commission, the line Ministry in charge of lands had in place an NLIMS strategy. NLIMS was conceptualized in Project for Improving Land Administration (PILAK) whose mandate was: safeguarding land paper records, developing business and IT architecture, modernizing the geodetic framework, parcel identification reform, develop land rent collection system, systematic conversion to RLA titles, develop other land administration systems and create public awareness. The basic building block in any land information system is the land parcel as identified in the cadaster. According to the International Federation of Surveyors (FIG) (1995), a cadaster is a parcel based, and up- to-date land information system containing a record of interests in land (e.g. rights, restrictions, responsibilities and risks). It usually includes a geometric description of land parcels (cadastral maps) linked to other records describing the nature of the interests, the ownership or control of those interests, and often the value of the parcel and its improvements. It may be established for fiscal purposes (e.g. valuation and equitable taxation), legal purposes (conveyancing), to assist in the management of land and land use (e.g. for planning and other administrative purposes), and enables sustainable development and environmental

1 The term ‘land administration’ as used here is based on a definition adopted by the United Nations Economic Commission for Europe (UN ECE). It refers to the processes of recording and disseminating information about the ownership, value, and use of land and its associated resources. This includes the determination of rights and other attributes of the land, the survey and description of land, its detailed documentation, and any other relevant information in support of land

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya protection. From this viewpoint it is clear that while NLC is charged with implementing and maintaining this system, there needs to be synergies and harmony in operations between the Commission, the land registries, national mapping agency, county governments, and treasury as they all need portions of the parcel based NLIMS. From this view, NLIMS is an all-encompassing system. Some of the initial offerings that can be packaged through the system include the ability by Kenyans, on the payment of applicable fees (using online or mobile solutions) to perform searches of land parcels, ability to make applications online for various services offered by the Commission and the Ministry, access to respective land data by professionals registered in the system and ability for persons to visualize spatial locations of various parcels and some of the information (attributes) held in the registry about the parcels. Additional functions to be implemented should include support for various workflows allowing the update of registry information, survey data and other transactions. Any system is as good as the data held in it and especially for a spatially supported one, as the NLIMS will be, the quality of data is paramount. A lot of this data is in analog (hard copy) format and it takes time to convert data. The conversion process should have in place various quality control and quality assurance measures. The most costly part will be this conversion process, but it requires to be hosted on capable and secure infrastructure.

Problem Statement Over the years, land administration in Kenya has been marred with a myriad of problems in what has been largely associated with a lack of an efficient, computer-based Land Information Management System (LIMS) (Kuria et al., 2016). The manual land administration system has led to missing land records (files), inadequate space for records, multiple allocation of plots, forgeries and altering of land allocation, encroachment, overlapping surveys, inefficient revenue generation and loss, rampant sub-division, amendments and falsification survey information on land titles.

The country continues to experience growth in all its sectors of development including land which is considered to be an important factor of production. For a long time land administration in Kenya has been a relatively stable paper- based system which has accumulated millions of records and is now deemed incompatible and ambiguous with the increased complex and high demand use of land. The paper-based system is cumbersome with complete human effort required. Updating of records for land transactions, sub- divisions, mortgages and other transfers depend on the tedious manual system which is painfully slow. Land records are sometimes not easy to find because there are hardly back-up copies created in the manual system. Updating of land records particularly spatial data is quite a challenge. Technical experts are forced to edit manually on the map that have rendered them illegible, hard to derive specific information, torn and dilapidated. Incidentally, there are no back up data of the sad maps.

Similarly, the manual system is time consuming where land records have to be shared from one department to the other to facilitate whatever request required by a client. The paper-based files move slowly to and fro the red tape structure. This decreases the productivity of land information handlers that has a lasting negative in the public eye. It can also take a long period of time to search for a particular record. Security is also not guaranteed to a certain level as the system allows ease in either copying, mishandling crucial information or loosing data. Therefore, it is about time that the paper-based system be replaced by an automated system which has proven to be efficient in land administration operations worldwide. This will also facilitate timeliness and ease in accessing land information in a transparent and reliable manner that will lead to securing more investments to meet the needs of the growing population.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Integrated Conceptual Framework

Figure 2: NLIMS Conceptual Framework (Adapted from NLIMS Proposal: NLIMS – from strategy to implementation, 2014)

The integrated conceptual framework of NLIMS is captured in 2 above. Land related data generated by the Commission, Ministry of Lands and Physical Planning (MoLPP) and County Governments through but not limited to, workflows for the different applications of the components of land administration (See Error! eference source not found.1) will be hosted in a data center, accessed by both database and GIS servers. It will be accessed through an online portal and mobile applications that feature both free and for-pay services. Payments made will be remitted to the bank or via the mobile money platform to avoid cash handling at the agency. For purposes of maintaining the data, administrative capabilities will be provided but which will be restricted to routine system maintenance and not tinkering with data held on the system.

Implementation Strategy The implementation strategy for NLIMS through the NLIMS Directorate mandated to spearhead the development of an NLIMS system has already undertaken a number of activities using a phased approach. The components of the integrated system are a Citizen Relationship Management (CRM) to handle all citizen related interactions with the Commission, Enterprise Resource Planning (ERP) to handle all resource management interactions and the National Land Information Management System (NLIMS) to handle and manage all land related data and information.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Due to the sensitive nature of the land subject in Kenya, high availability and secure system guaranteeing an all-round system availability is mandatory. This means that capable servers and network infrastructure, secure data repositories and backup mechanisms, which have to be accessed from the headquarters and also from the counties, have to be procured and commissioned. Given that most of the survey data and land records data are still in paper form, there needs to be concerted efforts in conversion to forms ready for digital consumption and dissemination.

This conversion goes beyond the mere scanning of documents and archiving of the same, but includes extraction of information held in these documents and their storage in a well-designed database (Nkote, 2015). The extraction of the information varies depending on the type of documents: for non-spatial records, these can be extracted by either manually reading off and entering the data in the database or by using Optical Character Recognition (OCR) technology to read, decode and store the information; for spatial records, these will be geo-referenced first and the spatial information captured by onscreen digitization or tablet digitization thereafter. In both of the cases, the elaborate quality control and quality assurance mechanisms alluded earlier will be enforced. Each parcel in existence has a history from when it was created to its current state and each stage has documentation supporting the various transactions.

From this it can be seen that to have the system fully implemented, procurement of the system is the first step (which is itself costly) acquisition and conversion of the data forms the main body of the system. It is anticipated that the initial procurement of the system, development of some custom applications, some data acquisition and conversion could well get into the region of 4 Billion Kenya Shillings spread over a three – four year period, with the overall costs easily going to upwards of 20 Billion Kenya Shillings by 2030 when all records and graphical (survey and planning) data will have been converted and entered into the system.

This project has come a long way, encompassing automation of all the land administration and management business processes into digital workflows that have been diligently built into the Integrated National Land Information Management System implementation featuring Land registration systems, Cadastral Framework, and CRM. This system is a noble effort to make access to land administration service more feasible and easy for Kenyan citizen and corruption free (however, this is debatable as to whether the technology will bet the vice). Key components of the system are web based and has a greater beauty of tight integration between the various components from Electronic Document Management System (eDMS), workflow engine to cadastral framework and the CRM side. No more pools of technologies at different departmental or organizational level. The systems leverage powerful and modern technologies to deliver the end solution such as Microsoft (MS) SharePoint, ArcGIS System (JavaScript API for Web and the Land Records Solutions), Custom built Document Management System (DMS), MS Dynamics CRM among other technologies. Also, it’s important to note that every enterprise solution must ride on a well modeled Information and for this, NLIMS is built on top of an adaptation of the Land Administration Domain Model (LADM) (Lemmen, 2012), the ISO standard for Land administration.

Implementation progress A number of activities have already been put in place to propel the realization of the NLIMS strategy. These are: i) Enhancement of the capacity to implement NLIMS The NLC was constituted slightly 3 year ago and it is thus, still a nascent organization with a huge mandate. It embarked on a fairly ambitious goal of establishing its various units through a vigorous human resource

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya recruitment exercise. Among the biggest beneficiaries of this effort was the NLIMS directorate, which now has a core team of 13 specialists in Spatial Information Management as GIS developers and Spatial Database experts. The model being considered by the Commission is where these core staff serve as Quality Control officers in assuring quality of outsourced works and helping secure the data and systems given the sensitivity of land information. ii) Development of integrated systems The NLIMS directorate developed system requirements (for phase one implementation) in conjunction with the ICT directorate. Recognizing the integrated way in which the NLIMS and the other systems of the Commission and the wider Government work, a tender for the supply of integrated systems solution was published.

The successful bidder was awarded the contract at the beginning of the year 2015 and has already developed and rolled out the ERP component of the integrated solution. The CRM part is steadily nearing completion as all the Functional Requirement Documents (FRDs) have been agreed upon and customization of the electronic Document Management Systems and CRM is well defined. The NLIMS component being specific to Kenya requires more work as the various workflows driving land administration and management processes in Kenya are largely unique, and the new legal dispensation in addition has modified some processes with respect to the actors, meaning that some of the processes have changed radically. Most of these workflows have been identified, the data model developed in line with the Land Administration Domain Model (picking relevant pieces that apply to the Kenyan context) and bearing in mind the principles of Cadastre 2014 (Kaufmann & Steudler, 1998), and programming and customization work is now in progress. iii) Establishment of a Spatial Data Conversion Laboratory The engine of NLIMS is a data collection, reparation, collation and conversion unit, from which maps, plans and other documents will be converted from. A tender for the supply of the equipment and software to run in the unit, christened the NLIMS Laboratory, was awarded and established. This Laboratory is now fully operational, with scanning and digitization work steadily going on. Some of the equipment in this laboratory includes; scanners, plotters, GIS workstations and barcode scanners.

Given the nature of spatial referencing in Kenya, where we have 3 main types of spatial reference systems, namely Universal Transverse Mercator (UTM), Cassini-Solder system and local coordinate systems, the Directorate in conjunction with the National Mapping Agency (Survey of Kenya) is developing a transformation scheme that will allow wall-to-wall seamless coverage of parcels countrywide. iv) Development of NLIMS Standards and Guidelines NLC is cooperating with counties in the development of specific components of LIMS at the county level and other agencies dealing with parcel data. The NLC realizing the need to allow various agencies to get along with implementing their own specific LIMS solutions, initiated the development of NLIMS standards and guidelines with input and contributions from stakeholders in the land sector. These standards and guidelines are to be used by data producers and stakeholders as they implement their versions of LIMS. This approach allows the various agencies to focus on delivering on their mandates that may include developing specific aspects of LIMS and allows the LIMS so developed to integrate with the NLIMS. The standards and guidelines have already been gazetted.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya GIS Integration The GIS Module in the Integrated NLIMS has been built using the ArcGIS System and the cadastral framework leverages the Parcel Fabric solution. The Parcel Information Management system is based on the Parcel Fabric data model enabled with NLIMS Data Model. The NLIMS Data Model is a local adaptation of the Land Administration Domain Model (LADM) to fit the Kenyan land administration system. The Parcel Fabric specific attribute and properties have also been customized to fit in the local cadastral framework. In the developed data model for NLIMS three primary classes have been created that is the Parcel Feature Class (Managed exclusively through Parcel Fabric feature class to control quality as far as accuracy and topological correctness is concerned, RRR (Rights, Restrictions and Responsibilities) table as the possible interests that can be registered on a Land Parcel and Party table to represent the records of the various entities that can have recorded interests of a particular land parcel These core classes are related to other tables that hold critical secondary information that pertains to various element in the core classes. The cadastral and parcel Information System for NLIMS are built to be managed primarily in the ArcGIS Workspace featuring an Enterprise Geodatabase running on SQL Database Server hosted at the NLC Data Center, ArcGIS Server for managing the Services consumed by other applications and the Map Viewers, and ArcGIS Desktop leveraging the Parcel Fabric Solution from Esri Land Records Solutions.

Of key necessity to the Commission is the ability to record and maintain the chain of evidence of the transitions on Parcel boundaries and attributes. Using Parcel Fabric solution for ArcGIS, the Integrated NLIMS has been built to keep the chain of evidence of the transitions on parcels of land in the database as Historic Parcels for the purpose of tracking the historic changes that occur in the parcel objects. Kenya as a country that guarantees Titles as the legal document for land ownership, the validity of the titles or rights on a parcel of land are based on the chain of evidence of previous survey and documents. The maintenance of the chain of boundary delineation and record changes over time is essential. New survey on land parcels cannot be conducted without surveyors accessing the evidence of the previous boundary delineation and the records.

The Cadaster and LIS have been integrated with other applications to allow consumption of the Parcel Information within these other applications used by other departments and institutions. Land Administration business processes have been digitized into workflows that have been implemented on SharePoint Foundation Server to deliver a public facing portal for accessing the processes as online services and an internal portal for administrative operation on the business processes. On the other hand customer service portal built using the Dynamics CRM for exposing query management services to the public has been delivered as a component of the integrated system. These key components of NLIMS have been integrated with the LIS/Cadastral component via Web Services to allow for consumption of the GIS Server services through them. With the integration Parcel Information can be accessed from any of the three components.

Conclusion NLIMS is an integrating solution, allowing various data providers to manage their data generation and maintenance on the platform, and allowing linking of all land related data, and the subsequent merging and sharing of these information from a unified platform. This allows data producers to manage their components according to the uniqueness and complexity of their workflows and structuring of their data. Adherence to the Standards and Guidelines will help in strengthening the integration of system components. Cooperation, collaboration and consultation are key to successful integration of all the units. Without genuine partnership driven by a common interest in reforming land information management, it will be difficult to integrate the individual elements. Mutual mistrust and unnecessary competition is detrimental

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya to realizing an integrated solution. This development has to have all the players speaking the same language and working to leverage the strengths that each of the unit possesses.

Digitization of all paper records is very important, but automation of all workflows is crucial to sustainable system. Automation of workflows is critical to ensuring sustainability of the system. In this way, data remains current as data is updated and maintained through the automated workflows.

Decentralizing of the implementation allows breaking of the huge system into smaller more manageable components. This allows for the disaggregation of financing, efficient allocation of project management efforts and ease of monitoring since smaller components would be in consideration.

Change management is critical to the successful usage of the system across the country. Rolling out of the system will in some cases require a transition from a paper based system to a digital system. This calls for the reassurance of officers that they will not be affected negatively, but rather that, these systems will enhance service delivery and increase their productivity. In addition, fitting training programs will help in easing absorption and usage of the system developed.

Reference FIG (1995). FIG Commission 7 Statement on the Cadastre. Retrieved from www.fig.net/commission7/reports/cadastre/statement_on_cadastre.html GoK (2012). The National Land Commission Act. Government Press, Nairobi GoK (2009). The National Land Policy. Government Press, Nairobi Kaufmann, J. & Steudler, D. (1998). Cadastre 2014: A vision for a future Cadastral System. Paper presented at the 1st Congress on cadaster in the European Union. Granada, Spain Kuria, D., Ngigi, M., Gikwa, C., Mundia C., Kibui S., Omondi, S., Thaa, B., & Macharia M. (2016). Improving the State of Land Administration on developing countries: a Case of LADM for Kenya – Opportunities & Challenges. Paper presented at the World Bank Conference of Land and Poverty Washington DC, 2016. Kuria, D. (2014). NLIMS Proposal: NLIMS – from strategy to implementation. NLC Press, Nairobi Lemmen, C. (2012). A Domain Model for Land Administration. University of Twente, Enschede Kote, N.I. (2015). Land Administration and Automation in Uganda Makerere University Business School, Kampala. Retrieved fromhttp://cdn.intechopen.com/pdfs-wm/37998.pdf

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Challenges of Developing Land Information Management Systems (LIMS) for County Governments in Kenya

ROBERT Wayumba and Ntonjira LIZAHMY Technical University of Kenya

Key Words: Land Information Systems, Counties

Abstract This paper describes the challenges of developing a Land Information Management Systems (LIMS) for County Governments in Kenya. In most developing countries land information is still held in paper format, which can be destroyed through wear and tear and can also be difficult to retrieve. In order to improve land administration services, there is a need to develop digital LIMS. In addition, according to the County Government Act, 2012 in Kenya, all County Governments are supposed to develop digital Geographic Information Systems (GIS) based Spatial Plans that should include land information. Despite the need for LIMS, there are multiple challenges that face implementation of the systems. In this regard, there is a need to study the challenges, as a means of averting them in future LIMS initiatives. This paper uses case study methodology to document challenges that were encountered in developing part of a LIMS for Kerugoya County. The results show the legal, social, political, technical and economic challenges that were encountered in the project. The conclusion is that there is a need to establish means of resolving the challenges if effective LIMS are to be implemented not only in County Governments in Kenya, but also in other developing countries.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Introduction Implementation of Land Information Management Systems (LIMS) can contribute towards economic growth and development in a country. In this case, a LIMS can be described as a computerized system for managing land records, which usually consist of property boundaries and related attribute data (Dale & McLaughlin, 2000). The LIMS can contribute towards development by: enhancing land ownership security, increasing access to credit, enabling development of land markets, reducing land disputes, enabling land taxation and land reforms among other possible benefits (Williamson, Enemark, Wallace, & Rajabifard, 2010).

Despite the possible benefits of LIMS, many developing countries continue to hold paper based land records. In essence, land records usually consist of maps and records related to the land. The maps are usually developed by cadastral surveyors and include the spatial extent of the land and its area (Larsson, 1991). The attributes related to the land normally include the proprietor’s name, any encumbrances on the land, size of the land and legal system in which the land is registered among other aspects (Simpson, 1976). These records are usually held in paper files which are susceptible to wear and tear and which can also get lost (Kuria, Ngigi, Gikwa, Mundia, & Macharia, 2016). If the paper records get lost or destroyed, very valuable information on land ownership and any encumbrances on the land may be lost forever.

Implementation of LIMS is also hindered by the low extent of formal land registration in developing countries. In general, land registration describes the method through which matters concerning ownership or other rights to land are recorded (Zevenbergen, 2002). The type of registration can be based on a deeds system, a title system or an improved deeds system (Simpson, 1976). According to a World Bank report that was released in 2003, in Sub-Saharan Africa countries, only about 10 percent of the land has been formally registered (Deininger & others, 2003). In 2013, a more optimistic outlook was provided for all developing countries in the World, in which only about 30 percent of the land has been registered (Zevenbergen, Augustinus, Antonio, & Bennett, 2013). Nonetheless, the figure is still very low. Hence, one of the reasons why most countries in Sub-Sahara Africa continue to be poor has been attributed to the lack of extensive coverage of land registration and lack of LIMS (De Soto, 2000). Therefore, there is a need to explain challenges that hinder implementation of LIMS, as a means of enabling solutions to be established, to allow for economic growth and development.

Methodology In order to explain the challenges associated with implementation of LIMS, case study methodology was selected as the main form of inquiry for this paper. The methodology was selected because it is suitable when a phenomenon under investigation is not easily distinguished from its context and it allows the use of qualitative analysis (Eisenhardt & Graebner, 2007). The methodology was also selected because it is a recommended form of inquiry for research on land administration issues (Çağdaş & Stubkjaer, 2009).

The case study that was selected is part of a project on implementation of LIMS for Kerugoya County in Kenya. In Kenya, according to the County Government Act, 2012, all County

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Governments are supposed to have Geographic Information Systems (GIS) based spatial plans. As a result, The Technical University of Kenya, carried out a pilot project on how part of the GIS, which deals with land records i.e. the LIMS, can be implemented. Thus, this paper provides some of the challenges that were encountered in the project. The main aim is to enable better implementation of LIMS, not only in the County, but also in other parts of Kenya and other developing countries. The following section shows the legal, political, social, technical and economic challenges that were faced in the project.

Results and Findings Legal challenge There were some legal challenges during the initial stages of the project. The first legal challenge was associated with contractual agreements between the university and the county. At the inception stage, there was a need for the two institutions to sign a Memorandum of Understanding (MoU). There was a need to agree on dispute resolution mechanisms, extension of time if the consultant did not deliver the project in time, and a need to resolve perceived loss of power by some institutions in the county. A major part of these challenges were resolved, and the two institutions were able to sign a MoU.

A key legal challenge was lack of a clear legal roadmap on how to implement the system. At the moment, there are no formal guidelines on how LIMS should be implemented in Kenya. As a result, there is no Act of Parliament that describes how the computerized land records would be managed as opposed to the existing manual records. In the manual system, the mandate of various actors in producing formal land records is clear. As an example, the Director of Surveys is responsible for authenticating cadastral maps, while the land registry is responsible for producing title deeds (Njuki, 2001). In the project, it was not clear how the digital records would be handled be handled by the various institutions, which created some hesitation on the project. At the moment, the National Land Commission (NLC) is in the process of developing Standards and Guidelines that will most likely be used to implement LIMS at both the National and County levels in Kenya (Kuria, Kasaine, Khalif, & Kinoti, 2016).

Another legal challenge is that there were many unresolved land disputes, which made it difficult to identify legitimate land owners. In the process of developing a LIMS, there is a need to capture land ownership records (Dale & McLaughlin, 2000). In the study area, identification of land owners was first carried out by the colonial and post-independence Governments of Kenya. At around 1960, the British Colonial government initiated a process of land adjudication in parts of Kenya (Sorrenson & others, 1967). The aim of the adjudication exercise was to identify the people who held rights to land and confer upon them ownership of land through formal registration. In 1963, The Republic of Kenya obtained independence from the British, and the independent government continued with the process of land registration in the country (Sorrenson & others, 1967). A major challenge is that after the first registration was conducted, succession has not occurred in most areas, hence current owners are not known. To this extent, there is a need for succession to be completed in the counties as a means of enabling LIMS that will be implemented to have accurate ownership information.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya

Social challenges In the implementation of information systems, social challenges are related to human factors that can hinder planning, development and utilization of the systems (Laudon & Laudon, 2004). As a result, implementation of LIMS requires an adequate assessment of user needs requirements (Goodchild, 2009). In Kerugoya, the views of the users and stakeholders were many and diversified. Thus, the consultant could not incorporate all the user needs within the limited time frame of the project.

A few of the users were uneasy with the changes that would be introduced by the digital LIMS. As stated earlier, in Kenya, various actors know their role in the process of developing and using paper based land records. A proportion of the employees in the government were uneasy that the digital system would make them redundant or require them to go through lengthy and expensive training, which they could not afford. Thus, in order for LIMS to be properly implemented in any jurisdiction, there is a need to allay the fears of existing staff members on the effects of computerization.

Political challenges Political challenges were also observed in the project. As much as these challenges could be categorized as social, they are isolated because politicians play a major role in determining development in their jurisdiction. According to Rakai, (1995), land information is a resource that can be used for political gain. On the one hand, politicians can use the ignorance of people on land information for political gain, to the detriment of the people. On the other hand, the information can be used positively for the benefit of the people (Rakai & Willlamson, 1995).

In the study area, some politicians were against implementation of the system because it was too expensive for the county to finance. Indeed, one of the major challenges that face introduction of LIMS in most developing countries is that they are expensive to implement (Williamson et al., 2010). However, on the other hand, the systems can contribute significantly to collection of revenue, hence, justifying implementation costs (Williamson et al., 2010).

A number of the politicians were concerned that the system would have detrimental effects on their political control. In this regard, the consultant had to carry out extensive sensitization of the potential benefits of the system, as opposed to the fears of the people. Eventually, the political challenges were overcome, and the project commenced. Nonetheless, the continued success of implementation still requires goodwill from the politicians.

Technical challenges There were also several technical challenges encountered in the project. The first technical challenge was associated with very low extents of fixed boundary surveys in the county. In Kenya, land registration is either based on Registry Index Maps (RIMS) or Deed Plans (Siriba, Voss, & Mulaku, 2011). In the areas where registration has been introduced, approximately 80 percent is covered by RIMS, which do not have any mathematical boundaries. The development of most of

National Government Track 98/141 Robert Wayumba, Ntonjira Lizahmy Challenges of Developing Land Information Management Systems (LIMS) for County Governments in Kenya

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya the RIMS was based on unrectified aerial photographs, which were not mathematically defined (Siriba et al., 2011). In contrast, a few parts of the country are surveyed and boundaries depicted on deed plans which have mathematical coordinates. A very large portion of Kerugoya County, like many parts of Kenya, is covered with RIMS, which are not mathematically defined. In this regard, it was very difficult to include the boundaries in an LIS, which requires mathematical coordinates.

Another technical challenge is that RIMS are not updated. According to the law in Kenya, every cadastral survey on land should be legally documented by updating survey plans or RIMS. In most parts of Kenya, the original registered land owners have subsequently passed on the land to their offspring or sold it off to other people. During the process of inheritance the land is usually divided to the offspring and during a sale, a portion of the land can be divided to the seller. The inheritance and sales are secured through informal agreements, or custom, as opposed to the formal system (Cotula & Chauveau, 2007). As a result, the spatial changes on the ground are not reflected in the RIMS. To this extent, when the consultant tried to input existing RIM data into the LIMS, there were many discrepancies with existing boundaries on the ground, which have not been mapped.

There was also a challenge in the numbering system of the parcels on the map. At the initial stages of the project, an assumption was made that the parcels would be sequentially numbered on the RIMs. However, when the work started, it was realized that there was no systematic numbering system in some parts of the county. In addition, some parcels on the ground did not have a known number. In this regard, there is a need to develop a unique and legally recognized numbering system for the parcels of land. Indeed, as stated by De Soto (2000), one of the reasons why “Capitalism” fails in developing countries, is because most business are operated without a legal description on where they can be found, hence cannot be taxed adequately (De Soto, 2000). To remedy the situation, the National and County Governments need to develop a unique geocoding system that can be used to identify each land parcel.

Another technical challenge was that at the time of implementation, no staff member in the county had been trained on how to operate the LIMS. A major source of failure of information systems is lack of adequately trained personnel (Laudon & Laudon, 2004). Thus, if the LIMS is to work, the County government must invest in training staff members on how to use a LIMS. The training can be in the form of short term or long term courses. In this regard, county officials who already have a background in spatial sciences can be taken for short courses on the use of Geographic Information Systems for land records management. If the county is able, it can also sponsor some people on long term courses on LIMS.

Economic challenges There were also economic challenges encountered in the project. In the process of capacity building on land administration, there is a requirement that people should invest in what they can manage (Williamson et al., 2010). Lack of adequate finances can hinder implementation of necessary projects such as LIMS. Implementation of an effective LIMS is a big financial undertaking for any country (Larsson, 1991). In the County, the finances were not enough to

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya implement a fully functional LIMS. As a result, the project was a pilot of what could be done if finances were fully available.

During the project, there were project overheads that had not been budgeted for. The project on LIMS was one of the first attempts at introducing LIMS in the counties and was very innovative. As stated earlier, it is also a legal requirement for the counties to implement GIS based spatial plans. However, because these projects are being implemented for the first time, and without standards and guidelines, there were bound to be some oversights. Thus, in the initial budget, the number of personnel and equipment for implementing the project was underestimated. As an example, during the user needs requirements, the finances budgeted for, were not adequate to administer the questionnaire to all users that were supposed to be sampled.

The process of releasing project funds was also very slow. A major recommendation on how to re- engineer land administration systems is related to reducing the number of steps required to obtain legal documents (Williamson et al., 2010). In the project, there were many officials required to sign documents before the release of funds. On the one hand, this was positive, because the number of people would discourage misuse of funds. On the other hand, too many bureaucratic processes slowed down the project.

Conclusion The main conclusion from this paper is that the challenges facing implementation of LIMS should be resolved if effective systems are to be implemented. The legal challenges should be resolved by developing adequate standards and guidelines on how to implement and manage LIMS at both National and County levels. In addition, the standards and guidelines should be translated into a law that can be legally enforced. The social and legal challenges can be resolved by adequate sensitization on the possible benefits of computerization. The technical challenges can be reduced by improving on the existing cadastral maps. In essence, there is a need to update all cadastral maps not only in the County but also in Kenya as a whole. Finally, the economic challenges can be resolved through proper project planning and management. In this regard, if the finances are limited, a systematic approach can be used for computerization, in which small portions of the County are digitized, as opposed to tackling the whole county as a whole. The authors hope that this paper will contribute towards implementation of effective Land Information Management Systems (LIMS) that will contribute towards economic growth and development in Kenya. The authors also recognize that further quantitative and qualitative studies should be carried out to find out more details on the challenges that hinder implementation of LIMS and how the challenges can be resolved.

References Çağdaş, V., & Stubkjaer, E. (2009). Doctoral research on cadastral development. Land Use Policy, 26(4), 869–889. Cotula, L., & Chauveau, J.-P. (2007). Changes in customary land tenure systems in Africa. Iied. Retrieved from https://books.google.com/books?hl=en&lr=&id=-

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya u8ka6Mu6SoC&oi=fnd&pg=PA1&dq=Changes+in+Customary+Land+Tenure&ots=AU XiQ2DLep&sig=532iqTBUJayKDdL0hCXRHr0JQBM Dale, P., & McLaughlin, J. (2000). Land administration. OUP Catalogue. Retrieved from http://ideas.repec.org/b/oxp/obooks/9780198233909.html De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. Basic books. Deininger, K. W., & others. (2003). Land policies for growth and poverty reduction. World Bank Publications. Retrieved from https://books.google.com/books?hl=en&lr=&id=- 3HWZigoZDMC&oi=fnd&pg=PR9&dq=Deininger+Klaus+2003&ots=3Pq_iW4Bte&sig =oXWHow4y9RJ4loF-MMIMLwUjWv8 Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. Academy of Management Journal, 50(1), 25–32. Goodchild, M. F. (2009). Geographic information system. In Encyclopedia of Database Systems (pp. 1231–1236). Springer. Retrieved from http://link.springer.com/10.1007/978-0-387- 39940-9_178 Kuria, D., Kasaine, A., Khalif, A., & Kinoti, S. (2016). Developing A National Land Information Management System – The Kenyan Strategy. Presented at the 2016 World Bank Conference on Land And Poverty, Washington: World Bank. Kuria, D., Ngigi, M., Gikwa, C., Mundia, C., & Macharia, M. (2016). A Web-Based Pilot Implementation of the Africanized Land Administration Domain Model for Kenya—A Case Study of Nyeri County. Journal of Geographic Information System, 8(2), 171. Larsson, G. (1991). Land Registration and Cadastral Systems: Tools for land information and management. Addison-Wesley Longman Publishing Co., Inc. Retrieved from http://dl.acm.org/citation.cfm?id=532788 Laudon, K. C., & Laudon, J. P. (2004). Management information systems: managing the digital firm. New Jersey, 8. Retrieved from http://www.academia.edu/download/6946733/10.1.1.130.4393.pdf#page=103 Njuki, A. K. (2001). Cadastral systems and their impact on land administration in Kenya. In International Conference on Spatial Information for Sustainable Development, Nairobi (Kenya) (pp. 2–5). Rakai, M. E., & Willlamson, I. P. (1995). Implications of incorporating customary land tenure data into a land information system. Australian Surveyor, 40(4), 29–38. Simpson, S. R. (1976). Land law and registration (Vol. 14). Cambridge University Press Cambridge. Retrieved from http://library.wur.nl/WebQuery/clc/186306 Siriba, D. N., Voss, W., & Mulaku, G. C. (2011). The Kenyan Cadastre and Modern Land Administration. Zeitschrift Fur Vermessungswesen, 136, 177–186. Sorrenson, M. P. K., & others. (1967). Land reform in the Kikuyu country. Land Reform in the Kikuyu Country. Williamson, I. P., Enemark, S., Wallace, J., & Rajabifard, A. (2010). Land administration for sustainable development. ESRI Press Academic Redlands, CA. Zevenbergen, J. (2002). Systems of land registration aspects and effects. TU Delft, Delft University of Technology. Retrieved from

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya http://repository.tudelft.nl/assets/uuid:44e404e9-c1e9-4c20-b1e1- 977ee9c11570/ceg_zevenbergen_20021111.pdf Zevenbergen, J., Augustinus, C., Antonio, D., & Bennett, R. (2013). Pro-poor land administration: principles for recording the land rights of the underrepresented. Land Use Policy, 31, 595– 604.

Biographical Details Dr. Robert Wayumba is a lecturer at the Technical University of Kenya. He holds a Doctor of Philosophy (PhD) from the University of Otago in New Zealand. He also holds a Master of Science in Land Management, from the Royal Institute of Technology, Stockholm, Sweden and a Bachelor of Science in Surveying, University of Nairobi.

Ms. Ntonjira Lizahmy is a final year student at the Technical University of Kenya, Department of Land Administration and Information.

Contacts Dr. Robert Wayumba Department of Land Administration and Information Technical University of Kenya Email: [email protected]

Ms. Ntonjira Lizahmy Department of Land Administration and Information Technical University of Kenya Email: [email protected]

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016|Acacia Premier Hotel, Kisumu, Kenya Utilities &

Transportation Ves Sites Selection Model for Ground Water Analysis and Mapping

John ESTHER and Njenga WAINAINA, Maxwell BARASA, Kenya Rural Focus Ltd.

Keywords: Groundwater, VES, Aquifer, Geospatial analysis, Suitability analysis.

Abstract Turkana is an arid area experiencing severe water shortages. The government of Kenya in collaboration with international donors speculated that there was an abundant supply of ground water that can sustain the whole country for the next 70 years and contracted a company to do ground water analysis and mapping. The process involved analyzing digital geological data, remote sensing imagery analysis, and creation of 3D basement formation model from structural contours and selection of most suitable sites for Vertical Electric Sounding (VES) survey to determine the availability and depth of aquifers.

A customized geospatial tool combining expertise from key hydrogeologists and geospatial analysts was designed in ArcGIS to perform site suitability analysis for the VES survey process. The tool was able to create analytical maps indicating areas with high ground-water potential. On performing the VES survey, over 90% of the sites analyzed in Turkana County within the zones indicated by the tool, shows availability of ground water within a depth of 500 meters from the surface.

Utilities & Transportation Track 104/141 John Esther, Njenga Wainaina, Maxwell Barasa VES Sites Selection Model for Ground Water Analysis and Mapping

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Introduction The problem of bulk water in arid and semi-arid areas has plagued most parts of Kenya for over half a decade. Some of the areas such as Turkana which is in the arid areas has had some of the worst effects of lack of water. In the years it has experienced severe droughts as river beds dry, this has presented the need to look for water underground. According to UNESCO the water problem is so severe that Turkana is one of the hottest, driest and poorest parts of Kenya and has been hit by devastating water shortage. In recent times various research groups including the government have done a number of studies in this county to try and alleviate the water deficit problem in Turkana County. The most notable work was the work done by RTI under UNESCO funding. RTI identified an aquifer in the Lotikipi where previous drilling had come up dry until the study showed drilling needed to be a bit deeper to get to the aquifer. The study showed that the aquifer could sustain the country in the next 70 years.

Study Area Turkana County is one of Kenya’s 47 counties with a population of 855,399 (KNBS 2009) covering an area of 77000 sqkm (approximately 13% Kenya’s surface area). The county is bordered by Marsabit County to the east, West Pokot and Baringo counties to the south-west, Samburu County to the south-east, Uganda to the west, South Sudan to the north-west and Ethiopia to the north-east. (Turkana County Government, 2013-2017).

Turkana County being a part of the rift valley has varied complex physiographic properties which comprise escarpments, mountain ranges, plains and swamps, and the lakes. Most of the mountain ranges are volcanic in nature and include Muruanachock hills, Lobur hills, Lokwanamoru hills, Sogot hills, Moggila hills and the Muruasigar hills mostly capped with rhyolites and olivine basalts. The plains are usually dry during the dry season but once the rain falls they become swampy. The biggest swamp is the Lotikipi swamp which stays soggy for the better part of the year. The plains are composed mainly of recent superficial deposits and silts. Turkana has the two lakes; Lake Turkana (formerly Lake Rudolf) is the largest permanent desert lake and the largest alkaline lake. It gets water from the Omo River flowing from the Ethiopian highlands, Turkwel River and Kerio River from Kenyan highlands. It is also fed by various seasonal rivers from Marsabit county of Kenya. The other lake is Lake Logipi which is a very shallow tiny lake to the south west of Lake Turkana (Turkana County Government, 2013- 2017). It is separated from Lake Turkana by a barrier volcanic complex which is composed of young volcanic e.g. ashes and phonolites. The expansive Uganda escarpment to the west of the county is a fault scrap composed of a basement system gneisses and pockets of tertiary volcanic (Walsh, Dodson, 1969).

Turkana County is classified as an arid and semi-arid area experiencing a maximum temperature of 37oC and a minimum of 21oC. It experiences a bimodal rainfall regime that doesn’t amount to much since the average annual rainfall ranges between 300 mm to 400 mm with rainfall averages hitting as low as 150mm. Even if the regime is classified as bimodal the pattern is at best unpredictable with some areas staying for a whole year without rainfall. The County is very windy (creating a modified hot and dry climate to be somewhat cooler) especially along the lake leading to even the development of Turkana Wind power plant. As an arid and semi-arid area, the county experiences clear skies most of the time all year round with scorching sun.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya The main form of livelihood is nomadic pastoralism (keeping cattle, donkeys, camels and goats). Other forms of livelihoods include fishing along the Lake Turkana and basket weaving. The economy of the county is driven by tourism, cattle sales and on the recent trend petroleum and wind power revenues.

The land cover of Turkana County is composed of vast expanses of bare hard rocks, shrubs and scattered trees.

Geology of Turkana is characterized by depositional superficial deposits of sandstone, grits, alluvial sands, conglomerates, limestone and siltstone ranging in age from Pleistocene to recent. Basalts, rhyolites, andesites, tuffs, ashes, nepehelinites and phonolites of tertiary period overly Archean basement (Walsh 1969, Dodson 1971, Joubert 1966, Fairburn., et al 1970)

Fig 1: Map of Turkana (courtesy of Turkana County Government)

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Datasets and Methodology Datasets The data used in the analysis includes digital (pdf) geological maps at various scales from the Ministry of Mining created from 1960 to 1988, Geological Map of Kenya with Structural Contours, topographical maps of Kenya at a scale of 1:250000 and Aster 30m resolution Digital elevation model data. The data was prepared, digitized and analyzed via ArcGIS software algorithms and scripts to perform overlay analysis.

Methods There are various approaches (both scientific and non-scientific) to determine the availability of ground water. This include; water dowsing, preliminary surveys, topography analysis and hydro geophysics.

(A) Preliminary survey involves visiting the area of study and doing a through desk study of the area. In this survey aspects of the area like where the springs have been or are located, where boreholes have been drilled and whether they were dry or successful and their yields, where the vegetation has always been green even on dry season and consulting local dowsing specialists if available. In this period, various datasets are collected including satellite imagery, aerial photos, seismic profiles, geological maps, elevation data, vegetation data and climate data. (B) Analyzing topography. Various physical features are analyzed in this step to give an indication of where the aquifers maybe located. Vegetation is analyzed using satellite imagery and/or aerial images in remote sensing software environment to determine; where vegetation is permanent and green during driest seasons, the dipping, outcrops and outlines of geological formations, lineation and faulting of the area and the elevation profiles of the area. (Kumar, 2014).

(C) Hydro-geophysics. This method involves measuring the electrical resistivity (capacity of a material to resist flow of electric current). It encompasses various methods which are selected depending on the geological context of the area, the depth of the aquifers needed to be identified and budgets available (Loke, 2001). The geophysical methods include:

Measuring resistivity using a direct current. In this method a direct electric current is sent into the geological structure being investigated using two electrodes and measuring the resistivity of the structure as the current penetrates to a specified depth (McNeill, 1992). This method is preferable for determining moderately deep aquifers in areas which are relatively flat and free of buildings. (Christensen et al, 1998)

Measuring reactivity by magnetic means. These methods measure the reactivity of soils to electromagnetic excitation by measuring electromagnetic signals due to magnetic induction phenomena. These methods cannot be used for all types of grounds or on aquifers that’s are more than 20m below the surface. These methods include isotope tracing method and proton magnetic resonance method.

After the examination of the task at hand, most of the methods concepts and ideas were used with additional geospatial analysis and techniques to determine ground water availability and the best places to perform geophysical investigations without excessive failed test points.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya The method adopted in this analysis was for identifying areas with potential for ground water availability on the basis of proximity of the basement system that creates pockets for water to be trapped and favorable geologies to store the water infiltrating.

Data Preprocessing First, all the maps were converted from .pdf to .tiff. This .tiff files were without any spatial reference information on them.

Second, the geological maps were georeferenced to their specific projection they were initially created in which Arc 1960 Datum is based on Clarke 1880 reference ellipsoid, Universal Transverse Mercator projection. Map geo-referencing as accurate as possible is needed to accurately trace the needed data and create digital vector data (Ellwood et al., 2016). For each map the points used as control points were all the cross points of the various gridlines. Due to inaccuracy of hand drawn maps, further control points were added by comparing the maps with current accurate data of various physical features like rivers and base of mountains to improve the overall accuracy.

Third, all the maps were projected to a reference system which was Geographic Coordinate system WGS 84 using the transformation of Arc_1960_To_WGS_1984_2 which is the recommended transformation for Kenya. The datasets were then projected to World Mercator Projected Coordinate system so as to conform to the raster datasets projections used to create outputs like hill shades.

Fourth, a Mosaic was created combining the various geological maps into one extended image. This mosaic aided in identifying the gaps in geology that were needed to be filled. These gaps include the upper part of the county (Kibish) which was updated via a geological map of The Sudan. The other gaps were left empty.

Fifth, a geodatabase schema was created to hold the anticipated geological formations from the maps. After the schema heads-up digitization of the mosaic commenced creating a wide range of geological formations in vector format. In the same way, the structural contours from the map were digitized. The structural contours in this map shows smoothened depth to the basement in kilometers.

Sixth, after the digitization was finished, all the gaps in the data were identified and filled up with the geological map of Kenya lithological dataset and the output clipped to fit to Turkana County borders. The geological data obtained was left with the naming of the original maps first and then after wards the datasets were merged where the geologists felt they were one and the same geological formation. For structural contours the digitization also included the addition of the depth field to act as an indicator of how deep the contour was below the surface. The structural contours were extended to reach the adjacent counties.

Surface Elevation Model Processing The 30m AsterGDEM V2 data was used in this project. The choice for this was based on the fact that it is the one of the high resolution open source elevation model existing for the whole world today. The elevation model was then taken through a low pass filter analysis so that it

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya becomes more accurate for morphometric analyses and physiography analysis (Tachikawa et al, 2011). Existing AsterGDEM data was downloaded from USGS website, mosaicked and clipped to fit to Turkana County. The raster was projected to World Mercator Projected Coordinate System. County hillshade was then created to depict hills and plains. The hillshade is used to limit the areas which are too steep to use the electrical sounding tool.

Creation of a Basement Depth Raster Conventionally elevation models are mostly used for depicting only surface topography. This elevation models can be created from point elevation data or contours using various interpolation techniques e.g. Inverse Distance Weighing and Triangulated Irregular Network. These methods use near neighbor idea where it is assumed points that are closer are more related than those far away (Fisher et al, 1987). In order to create a higher accuracy elevation model, some interpolation techniques that uses both local and global interpolation methods is needed. One of the best interpolation methods used today is the ANUDEM program that calculates regular grid elevation models with sensible shape and drainage structure from arbitrary large topographic data sets (Hutchinson 2008, Hutchinson and Gallant 1999, 2000). The inputs for the ANUDEM algorithms can be point elevations, elevation contours, streamlines, sink data points, cliff lines, boundary polygons, lake boundaries and data mask polygons which have a value field indicating the elevation of a surface from an arbitrary surface e.g. the sea level.

The ANUDEM uses algorithms that apply drainage enforcements and elevation tolerance constrains to adjust the accuracy of the elevation model in relation to the accuracy and density of the input elevation data (Hutchinson 1989).

Since the ANUDEM algorithms create very high accuracy DEM, it was adopted as the main method to use in this analysis as opposed to other interpolation methods. This method was used in an ArcGIS platform as a tool named Topo to Raster. The Topo to Raster tool is an interpolation method specifically designed for the creation of hydrologically correct digital elevation models (DEMs) adopted from the ANUDEM program. It takes all the data formats compatible with the ANUDEM algorithms and performs environmental modelling as discussed by Hutchinson (2000, 2008, 2009, 2011). As a tool the Topo to Raster interpolates elevation values from a raster while imposing constrains to ensure a correct representation of ridges, streams and sinks from contour data. The tool uses an iterative finite difference technique by having the computational efficiency of local interpolation methods e.g. IDW but without losing the surface continuity of global interpolation methods e.g. Kriging. Currently the method was adopted as the best to depict the structure of the basement from contours.

After each of the structural contours was digitized, a value was added that shows the depth of each contour below the surface. The Geological Map of Kenya with structural contours has data that depicts a smoothened depth to the basement for Kenya in kilometers (Government of Kenya, 1987). The kilometer value was converted to meters and used in the topo to raster tool. Since the tool enforces drainage on the resultant elevation model and removes inconsistent sinks, the tool was made to avoid enforcing since there are breaks in the basement caused by volcanism that should not be filled up. The final result of the process was clipped to fit to Turkana County from the bigger interpolated surface which was extending to other couties and countries.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Raster Difference Raster difference is used to calculate the difference of the surface contour to the basement contour to form the basement elevation model based on the height above sea level. The basement elevation model represents an elevation above or below sea level. In this model to be able to determine the depth to the basement from the surface we need a way of creating that difference data. Since all the raster datasets are in the same resolution and alignment, the raster map algebra algorithms were used to create a raster showing the elevation of the basement above or below sea level. Map Algebra is a simple and powerful algebra with which you can execute all Spatial Analyst tools, operators, and functions to perform geographic analysis (Esri, 2015). In this case the formula used was: ASTER DEM –DEPTH RASTER = BASEMENT DEM.

Geological Reclassification Groundwater flow in aquifer is dependent on permeability and porosity of the water bearing material that utilizes Darcy’s law, permeability is a measure of ability of a material to transmit water. For instance, sandstones may vary in permeability from less than one to over 50,000 millidarcys (md), permeability is more commonly in the range of tens to hundreds of millidarcies. A rock with 25% porosity and a permeability of 1 md will not yield a significant flow of water. Such “tight” rocks are usually basement and other granitic rocks (Bear.,et al 1972). In Turkana, aquifers are found in alluvial, sandstone, grits and basaltic formation.

Turkana geological formations were classified according to their permeability and porosity. This generated a raster showing suitability of various geological formations to contain ground water.

Previous groundwater mapping done in this area (RTI 2013) classified alluvial aquifers as the best yielding aquifers in the region. This study also prioritizes alluvial and sandstone aquifers as best yielding aquifers due to their porosity and permeability, however sandstone porosity decreases with increasing clay content. Turkana Grits which consists of sandstones, limestone, conglomerates, Kalapata beds is rated high in terms of groundwater occurrence and movement (Walsh 1969)

Weighted Overlay Analysis. Overlay analysis is a technique for applying a common scale of values to diverse and dissimilar inputs to create an integrated analysis (Esri, 2015). It combines various spatial analytics algorithms, models, datasets and techniques to create favorable outputs of a given phenomenon on the earth surface. The analysis follows a general order of process which is; define a problem, break the problem into sub models, determine the significant datasets, transform the datasets to fit into the analysis process, weight the input datasets depending on their influence to the phenomena, combine the datasets and finally perform analysis. (Mayfield C.J, 2015). Weighted overlay analysis assigns high values to favorable conditions for the phenomena being analyzed. The method employed in the study to identify the suitability of aquifer potential was employed based on the available data and time. The model builds on the fact that for an aquifer to be present it is largely a factor of geology and recharge. The geology plays a very major role since it is the one that retains water where it is favorable. This method employs geology as the major contributor with some geologies retain water while others retaining none. The other factor

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya employed is structural contours that describe the profile of the basements which are impermeable which thus retains water in pockets when the overlaying geology is permeable and has high water retention capacity.

The method adopts a raster analysis process which overlays and adds weight to different geologies and depth of the basements. The method builds up on key knowledge of hydrogeologists who described how they deduct the potential for aquifers and geospatial analysis expertise to combine the geological explanations of the hydrogeologists to create a model for determining the aquifer availability on a larger scale. In essence the method has been verified and is based on technical hydrogeological knowledge.

The process starts with calculating the influence of the various geological formations to ground water occurrence in the area (Jones, 1985). From the preliminary survey period weights were assigned to the geologies based on the available springs and boreholes. These weights were on a range from 1 to 10 which shows the order with which water has been known to occur in this areas.

10 shows the areas with the highest potential for storing ground water while 1 shows the least favorable geologies for retaining water. E.g. Turkana grits and sandstones take the highest priority since majority of the boreholes with high yield in the area are drilled into them.

The geologies are converted to a raster using the priority field as the value. This gives a raster with 10 values which are then reclassified to nine classes to fit into the weighing model.

After the reclassification process then the two datasets are ready to be weighted together. From consensus of the hydrogeologists, the basement was given a weight of 15% and the geology a weight of 85%. This is because the geological formations are the aquifer bearing materials with the basement as the benchmark where all overburden materials are categorized as aquifers. (Hussein.,et al 2016).

Results and Discussions The main objective of the process is to identify areas with high potential for underground water availability at a depth of not more than 600m from the surface. The suitability output is then to be used as a guide to perform Vertical Electrical Sounding to determine the availability of the aquifers. Hydro-geologically, groundwater occurrence is controlled by geological conditions including lithology and structures (Ismael. et al 2016). From the analysis a scale of 1 to 9 was generated to represent areas with high potential depending on the proximity of the geologies to the impermeable basement rock. Nine represents the most probable area to perform electrical sounding while 1 represent areas where potential for underground water is very low.

The basement elevation model is shown in fig. 2 from the model we can be able to conceptualize and view the profile of the basement system in Turkana County. The profile is a good indicator of the basins with water.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Fig 2: Basement elevation map

The reclassified geological map (Fig 3) shows the suitability of the geological formations to store underground water. It can be noted that the geological formations on top of hills are least favorable as compared to those on the foot of the hills. This owes to the fact that most of this hills are capped with rock outcrops thus water flows rapidly without percolating to collect at the foot of the hill where debris and sands have collected over time and been buried.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Fig 3: Classified geology map

The output of the suitability model was then compared to geology before commencing to select the areas to perform Electrical sounding analysis. Further analysis meant that a buffer of 1.5 kilometers was created around all the sandstones, conglomerates and Grits since they have a tendency of dipping and expanding below the other recent geologies for more than two kilometers from where they are observed on the surface. (Dodson, 1971). Furthermore, the hilly areas were noted by creating a hill shade to depict the hills (Fig 4).

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Fig 4: Hillshade map depicting hilly areas

The approach of verifying results of the GIS model generated results was through observing location of previously drilled high yielding boreholes and analysis of targeted geophysical electrical resistivity sounding data for the area (RTI 2013-2014). Electrical resistivity measurement is considered the most suitable for groundwater prospecting since it determines the sub-surface material properties, VES data analysis was done using resistivity software Interpex I1D.

Electrical sounding was performed on 102 selected sites based on analysis output. The priority areas were picked from a suitability level of 5 to 9. In areas where sandstones and grits are present they were extended to a 1.5 kilometer buffer even if the suitability model was less than 5. Of the 102 tested sites. Only 8 returned dry results representing 7.84% of the tested sites. On further analysis of the results 5, representing 4.9 % of the tested sites were discovered that the geology on the geological maps was different from what was observed in the field.

At the final step the GIS analysis confirmed presence of aquifer in the mapped areas where confirmatory drilling was done and found to be high yielding. Results of analysis shows aquifer presence in Lotikipi, Lodwar and Napuu which are part of the areas where suitability study

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya indicated presence of groundwater (Fig 5). The analysis showed unconsolidated sands and gravel which are basin filed aquifers provided best areas for aquifer occurrence where the semi permeable basement acted as the ‘basin’.

Fig 5: Suitability map depicting suitable areas to perform VES analysis

Conclusions. In conclusion, the suitability model presents a blueprint for future groundwater exploration using GIS analysis that yield best targeted geophysical survey areas as compared to carrying out blanket geophysical investigations. Limitations of this study was the other factors that control groundwater occurrence and movement such as porosity, fracturing, fault zones grain Utilities & Transportation Track 115/141 John Esther, Njenga Wainaina, Maxwell Barasa VES Sites Selection Model for Ground Water Analysis and Mapping

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya size and sorting of particles that were not included. Further modification can thus improve its overall accuracy if inclusion of this factors is adopted.

Acknowledgment. Authors are grateful to Ministry of Mining, Govt. of Kenya, County Govt. of Turkana and WRMA for providing necessary support during the study. Authors also acknowledge ESRI Eastern Africa for support on the use of their software and literatures.

References Christensen, B.N. and Sorensen, K., 1998. Surface and borehole electric and electromagnetic methods for hydrological investigation. Eur. J. Environ. Eng. Geophysics. 3, 75-90. Dodson R.G, 1971, Geology of Northern Turkana Degree sheet 18, Geological Survey of Kenya, Report No.87, Ministry of Natural Resources, Republic of Kenya Ellwood, E.R. et al., (2016). Mapping Life – Quality Assessment of Novice vs. Expert Georeferencers. Citizen Science: Theory and Practice. 1(1), p.4. DOI: http://doi.org/10.5334/cstp.30 ESRI 2015. ArcGIS Desktop: Release 10.3. Redlands, CA: Environmental Systems Research Institute. Fisher, N. I., T. Lewis, and B. J. J. Embleton, 1987, Statistical Analysis of Spherical Data, Cambridge University Press. Hussien M. Hussien , Alan E. Kehew a, Tarek Aggour, Ezat Korany , Abotalib Z. Abotalib , Abdelmohsen Hassanein , Samah Morsy (2016) An integrated approach for identification of potential aquifer zones in structurally controlled terrain: Wadi Qena basin, Egypt, published in Catena Journal 2016 Jones M.J. , 1985, The weathered zone aquifers of the basement complex areas of Africa, Quarterly Journal of Engineering Geology and Hydrogeology, v. 18:35-46, Geological Society of London. Kumar C. P., 2014, Groundwater Data Requirement and Analysis, Munich, GRIN Verlag, http://www.grin.com/en/e-book/281602/groundwater-data-requirement-and-analysis Loke, M.H., 2001. Tutorial: 2-D and 3-D electrical imaging surveys. Course Notes for USGS Workshop "2-D and 3-D Inversion and Modeling of Surface and Borehole Resistivity Data", Torrs, CT. Malczewski, J (2006). GIS-based Multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20, 703 – 726. Mayfield, C. J. (2015). Automating the Classification of Thematic Rasters for Weighted Overlay Analysis in GeoPlanner for ArcGIS (Master's thesis, University of Redlands). Opeyemi J. Akinrinade , Rasheed B. Adesina,(2016) Hydro geophysical investigation of groundwater potential and aquifer vulnerability prediction in basement complex terrain – A case study from Akure, Southwestern Nigeria. RTI. 2014, Taking stock of groundwater Discovery In Turkana (Kenya): The socio-economic impact of WATEX exploration one year later (September 2013-2014). T. Tachikawa, M. Hato, M. Kaku and A. Iwasaki, 2011, "Characteristics of ASTER GDEM version 2," Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, Vancouver, BC, 2011, pp. 3657-3660. Doi: 10.1109/IGARSS.2011.6050017

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Walsh J., Dodson R.G, 1969, Geology of Northern Turkana Degree sheet 1, 2, 9 and 10, Geological Survey of Kenya, Report No.82, Ministry of Natural Resources, Republic of Kenya

Biographical Notes John Esther received his BSc Degree in Geography from Egerton University in 2014. He did short Courses in GIS at ESRI Eastern Africa late 2014 and has been working since 2015 in Rural Focus Ltd as GIS Analyst.

Njenga Wainaina has a BSc. Degree in Geomatic Engineering and Geospatial Information Systems from Jomo Kenyatta University in 2013. He is currently pursuing MSc. Geospatial Information Systems and Remote Sensing. He is currently working at Rural Focus Ltd as a Surveyor/GIS officer.

Maxwell Barasa received a BSC Degree in Geology from the University of Nairobi in 2008, he did a certificate course in GIS and Remote sensing at Regional Centre for Mapping of Resources for Development. He also has a postgraduate Diploma in Hydrology from the University of Nairobi. Maxwell has 7 years’ experience in hydrogeology and has worked with Rural Focus for 5 years as an assistant hydrogeologist. He is currently pursuing Msc. Applied Geophysics.

Contacts John Esther Rural Focus Ltd P.O.BOX 1011 Nanyuki, Kenya [email protected] www.ruralfocus.com

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Apply Geography to Your Work and Make Better Decisions

Adding a location aspect to your projects gives you more insight into your data, improves planning, and helps you work more efficiently. GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example

Gertrud Schaab, Germany Karlsruhe University of Applied Sciences (HsKA), Faculty of Information Management and Media (IMM)

Key words: forest conservation, interactive visualization, environmental education, capacity building

Abstract Fifteen years of engagement for and in Eastern Africa has resulted in a myriad of activities, either research driven or related to capacity building. Geodata processing offers here versatile opportunities for cooperation, as the spatial reference enables inter- and transdisciplinary approaches. The paper provides an overview on the past achievements resulting from close collaboration with Kenyan and Ugandan counterparts. However, the focus is on more recent examples from the Kakamega-Nandi forests area in Western Kenya. While the processing of data from disparate sources was achieved by means of ArcGIS and remote sensing approaches, an open source software-based Web GIS tool now combines the spatially related scientific findings on the forest use history for visualization and exploration by anyone. The participatory development of environmental education tools, engaging many stakeholders from the local communities, resulted in various playful means. Some help to make a start in passing on abilities in regard to map reading, some to sensitize and steer discussions on the need of forest conservation. Finally, a university cooperation targeted a streamlined GIS teaching across the various university departments by jointly developing teaching material which includes geodata of regional relevance.

Cross-Cutting Issues Track 119/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Introduction Fifteen years of engagement for and in Eastern Africa has fostered many activities, either research driven or related to capacity building. The more than 50 written publications which have resulted so far are proof of this engagement. Geodata processing has offered here versatile opportunities for cooperation, as the spatial reference enables inter- and transdisciplinary approaches. The paper provides an overview on the past achievements resulting from close collaboration with Kenyan and Ugandan counterparts. However, the focus is on more recent examples from the Kakamega-Nandi forests area in Western Kenya.

It all started with the BIOTA East Africa project, funded by BMBF (German Federal Ministry of Education and Research) from 2001 to 2010. About 10 project groups investigated the impact of fragmentation and human use on the biodiversity of Eastafrican rainforests. The overall goal had been to come up with recommendations for a sustainable use of biodiversity. By considering three upland rain forest areas in Kenya and Uganda, comparisons were possible (see www.biota-africa.de/reg_east_intro_ba.php?Page_ID=L800 _04_01). Kakamega Forest in western Kenya served as focus study site. Kakamega Forest is known for its high biodiversity. Together with the Nandi Forests it once formed one continuous forest block. But 60% of natural forest cover got lost over the past 100 years. Being placed in one of the most densely populated rural areas of Kenya, the forests experience severe pressure. There is thus need to create awareness for forest conservation, also as many people depend on the forest’s resources (Mitchell et al., 2009).

Within the research framework, subproject E02 provided support by means of GIS and remote sensing. Our research questions were linked to investigating longterm forest cover change (e.g. Lung & Schaab, 2010) and forest use history (Mitchell, 2011). The findings helped to study their impact on biodiversity via spatial extrapolation of biological field data (e.g. Lung et al., 2012) and scenario simulation demonstrating if e.g. tree plantations could compensate forest loss (Farwig et al., 2014). As today’s forest is heavily shaped by man, research moved also into the surrounding agricultural landscape. Here, we derived information on farmland use and run livelihood scenarios (Lübker, 2014). In regard to activities related to capacity building (Schaab et al., 2009b), we got actively involved in forest management planning assuring that the scientific findings of the BIOTA East Africa project would be of use for implementations on the ground. A couple of GIS and geodata use courses had been offered to the counterparts or local stakeholders. BIOTA-East became especially known for its training of PhD and Masters Students. As a major measure to sustain the started activities, the Biodiversity Information Centre (BIC, www.iaf.hs-karlsruhe.de/gvisr/bic/) was set up in Kakamega Town. It encompasses a GIS unit with all the many geodatasets processed during BIOTA times.

The processing and application of geodata enabled the integrated analysis of diverse field data, their extrapolation in space and over time, as well as recommendations for a sustainable use and conservation of Kakamega Forest. About 500 geodatasets are available at the BIC, of which about 300 are related to the Kakamega Forest or Kakamega-Nandi forests area. The information centre is meant to support and empower local stakeholders. We are aware that handling of geodata requires expertise. Therefore, with the progressing of the BIOTA project visualizations for the communication of the often also interdisciplinary results gained increasing importance (Schaab et al., 2009a). A major impact had the publication and dissemination of The BIOTA Cross-Cutting Issues Track 120/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya East Africa Atlas. Rainforest Change over Time (Schaab et al., 2010). The atlas is special as it addresses three distinct user groups (Schaab et al., 2011). However, map reading literacy does not seem to be a common skill in Eastern Africa. This observation has guided our efforts in the more recent years.

More Recent Activities A Web GIS-based viewing tool on forest use history For tracing use histories of East African forests, data from disparate sources was processed by means of ArcGIS and remote sensing approaches. Extending time series based on satellite imagery with historical aerial photography and old topographic maps enabled for a reaching back beyond the start of any commercial-scale exploitation of these forests. Further information was gained from forestry records as well as archive materials. The deduction of land cover from village names, interviews with the oldest people living adjacent to the forests as well as soil pollen analysis added further valuable information. Via the spatial reference all data could be integrated and jointly interpreted (Mitchell, 2011).

A visualization tool now allows for a combined study of the resulting scientific text and the processed geodata (Weist et al., 2013). Realized as a Web GIS-based viewing tool, it combines the spatially related scientific findings on the forest use history for visualization and exploration by anyone. The tool consists of three components (Fig. 1): a text window for reading the scientific text, a map window displaying the geodatasets as well as the geodata quality diagram. Implemented functionality allows for navigation within the text, from where hyperlinks open the map window to display the relevant geodatasets. The user can change between the text and the map windows via tabs. For navigation within the map view the commonly expected functionalities are available. In the table of content (TOC) the layers to be displayed can be selected. Further geodatasets can be added from a predefined list. Where needed, the user can open a legend. For each geodataset, meta information is provided. And finally, one geodataset can be selected at a time for visualizing its six quality parameters in the quality diagram (Huth et al., 2008). If the user is not yet accustomed with how the visualization works, an explanation of the quality diagram can be accessed.

The application was implemented based on free OpenSource GIS technology and is database- driven: In a PostgreSQL database all required details on the geodatasets (information on quality parameters and for display mainly), map extents and geodata layers per text link are stored.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Fig. 1: Web GIS-based viewing tool for a combined text and geodata study. The tool integrates a geodata quality diagram.

The PostGIS spatial extension also enables the storing of vector datasets in the database. UMN Mapserver is used for rendering the raster datasets into PNG graphics. Realized as a client-server architecture, a get-request for the map, text and values is evoked when a hyperlink in the text window is clicked. As response SVG, XTML/JavaScript and PNG data is sent. Vector data is transferred in SVG geometries on demand, while JavaScript enables interactivity.

The scientific text builds on geodata-based forest cover narratives for twelve case studies pointing to the underlying causes and drivers. By further including spatially-explicit indices for the three investigated forest areas, local versus commercial disturbance can be compared with forest cover change, thus adding to understanding of the various impacts (Mitchell. 2011). The tool, therefore, serves the purpose of documenting and presenting the particular research results. It enables the working with the gathered data/information in an interactive environment with features/functions exceeding those commonly supported by a Web mapping system. Especially the dynamic geodata quality diagram has to be named here. With about 300 spatio- temporal geodatasets having been judged for their quality covering here six distinct quality parameters, this unique collection can serve as a concept in particular for those geodata collections which include a historical dimension. Overall, the tool facilitates the direct tracing of scientific statements/conclusions made in the text and offers thus the opportunity for scientists to gain new insights. As such, a comprehensive visualization and information tool on five Eastern African rainforests in Kenya and Uganda has become available.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Developing environmental education tools Increased knowledge leads to awareness, and both together stimulate action (Hungerford & Volk, 1990). In order to create awareness for the Kakamega-Nandi forests ecosystem, environmental education tools based on geodata were considered useful. By following a participatory development which engaged many stakeholders from the local institutions and communities, various tools were jointly developed. Here, playful means allowing for informing in an entertaining way had been favoured. For a more detailed description see Paul & Schaab (2015). Some of the tools help to make a start in passing on abilities in regard to map reading, some to sensitize and steer discussions on the need of forest conservation. The combination of playful approaches with mapped information appears promising for in particular creating the needed map reading literacy. In any, ideally participatory natural resource management and planning, geodata is considered a prerequisite. Implementation aims at analogue as well as digital versions. The analogue versions were to be produced first to be locally applicable also in environments without electricity and technical devices. In order to counteract the so-called digital divide, currently also digital versions are produced acknowledging their high attraction.

For successful environmental education programs the participation of stakeholders in the development process is a requirement (Athman & Monroe, 2001). We had opted for ‘participation by consultation’ as most suitable strategy. Four iterations served the purpose of enhancing the anticipated tools. Representatives of almost 40 stakeholder institutions tested four tools. During the first stakeholder workshops the tools were introduced, tested and assessed within focus groups by means of demonstration prototypes. In the second stakeholder workshop, evaluation of the further enhanced tools was based on focus group discussions and questionnaires. The goal was to learn from insights of those representing the multipliers of the tools when in use later. Finally, end-user testing addressed various potential user groups and were based on observations and semi-structured interviews by means of the previously tested questionnaires. This way, final prototypes could be achieved serving final production.

The following four analogue tools could be produced: Flipbooks using cartoon drawings relate to the four major forest threats. A story each on the harmful forest use and, after turning the flipbook over and using it in the opposite direction, on a sustainable use instead, exemplify the potential of behavior change. Here, however, no geodata is involved. Leaflets on nature trails make use of geodata and are meant to support ecotourism. Their link to environmental education is within the development process itself. Besides, two games were developed. The Local Forest Use Card Game works similar to the Memory card game which is widely enjoyed in Europe. The players have to pair cards on 27 forest uses identified for Kakamega Forest. When a user finds the matching second card, it presents the rules in regard to the particular forest use including reasoning and alternatives. A small map allows for locating the restricted areas. This environmental education tool proved to be very much liked as it informs in a playful manner. The Forest Cover Change Jigsaw Puzzli is more difficult and requires an instructor. It is accompanied by a narrative on forest change in the Kakamega-Nandi forest area. Here, users of an advanced learning level can visually experience the landscape’s changes while gaining knowledge on reasons for the tremendous change. This game too, showed high potential in steering discussions among the participants. Of all three tools based on geodata (Fig. 2), the jigsaw puzzle demanded the most sophisticated cartographic skill, this for coming up with puzzle pieces. ArcGIS was used here for the manually performed cartographic generalization. Cross-Cutting Issues Track 123/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Fig. 2: Final prototypes of three environmental education tools which build on geodata.

The environmental education tools are meant for non-formal education, where learners choose to learn. Here, the chance is to involve also adults and less-educated people who often are the important decision makers in rural areas (Pandey, 2006). The aims 1) of creating awareness for forest conservation (keyword ‘sustainable forest use’) , and 2) of building and enhancing map reading literacy (keyword ‘spatial citizenship’) are likely to benefit from the playful approaches. But the success depends on disseminating and making the tools widely available. Here, further workshops are required for training multipliers and instructors.

Streamlining GIS teaching across universities The experiences made during the BIOTA East Africa project had revealed that there is still a long way to go in Eastern African society for tapping the full potential of geodata and thus gaining its full benefits (cp. Schaab, 2007). Weaknesses exist in the application of geodata which reaches well beyond the mere creation of maps. Therefore, in addition to having set-up a GIS unit of use of all stakeholders and equipping it with the many geodata having arisen from the project, and the dissemination of diverse map-based information sources, we aimed at a more sustainable capacity building. A university cooperation served the purpose of passing on skills in training the next generations of decision makers. This way also institutions like the Biodiversity Information Centre will be strengthened and the many geodata available will be used in a sustainable way (cp. Schaab, 2015).

Funded by DAAD (German Academic Exchange Service) with finances from BMZ (German Federal Ministry for Economic Cooperation and Development) a partnership between Karlsruhe University of Applied Sciences (HsKA), Masinde Muliro University of Science and Cross-Cutting Issues Track 124/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Technology (MMUST) and Makerere University (MAK) was established. Taking advantage of knowledge transfer from Germany to Kenya and Uganda in regard to educating students in geomatics technology, the harmonization of GIS teaching would allow also for mobility between Eastern African universities. Under the project name ‘UnivGisKoop’ (see www.iaf.hs- karlsruhe.de/gvisr/project/univgiskoop.html) progress has been made in streamlining up-to- date GIS teaching into existing curricula structures across various departments of the two East African universities over the past four years. Emphasizing a regional focus, geodata use applications of regional relevance were considered during the joint development of adequate teaching material. At MMUST a GIS lab was established via additional funding by DAAD which allows the teaching of a higher number of students in the practical usage of GIS functionality.

Fig. 3: Impressions from workshops and final conference within the UnivGisKoop project which streamlined and promoted enhanced GIS teaching.

Most importantly, teaching materials for three modules were elaborated which include lecture notes and exercises, the latter applying ArcGIS for Desktop. Besides ‘GIS basics’ and ‘Advanced GIS’, a module on ‘Geodata use’ was deemed beneficial to provide the ground for an effective teaching of GIS theory and techniques. The joint development of harmonized GIS content for use in undergraduate and graduate programmes across faculties and universities has led to enhanced capacities related to resource materials and delivery, the latter covering knowledge as well as skills. Concepts of integrating the modules into the curricula have already benefited curricula reviews at the two target universities. For reaching out and visibility an Cross-Cutting Issues Track 125/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya international conference on GIS was organized in 2015. Although funding ended, the project partners agreed that the established partnership can further serve as springboard for more cooperation. Fig. 3 provides impressions from the project workshops and the conference.

The project allowed supporting those responsible for educating a large pool of young people. Integrating GIS teaching into various university curricula offers the chance for enhanced, modern study programs fostering integrated interdisciplinary approaches based on state-of-the- art techniques. However, computer labs dedicated to and suitably equipped for professional geodata processing require the full institutional backing. Applying geodata of regional relevance provide a closer link to the regions the universities are placed in and their specific environmental challenges. Thus, geomatics can serve as empowerment tool for a sustainable development of the region.

Conclusions By providing an overview plus referring to three efforts in more detail, the intention has been to demonstrate that geographic information (GI) allows for far more than creating pretty maps which make reports look more beautiful. GI-diversity encompasses a versatility of different data sources, offers a myriad of application possibilities, and thus benefits plenty of real-life situations or questions. However, of major importance is to adequately address the potential users. Here we have provided one example of addressing scientists by providing easy access and a working environment for exploration of geodata which is available but less known and had resulted from laborious data gathering and compilation. The Web GIS application is meant to stimulate the use of this data. In the second example, the ordinary people are the addressees, but in their role as stakeholders of the protected forest resources or as representatives of local institutions/organizations. As map reading literacy cannot be taken for granted, playful environmental education tools building on geodata have been developed, which are meant to create ‘spatial citizenship’ for participating in informed decision making for natural resource planning. The third effort described is aiming at a much broader impact in regard to both people reached in the longterm as well as coverage of methods for geodata processing and interpretation. Hands-on how to train the next generations in benefitting from making use of the spatial reference and GI-technology will contribute to empowering many more people. As such, since the BIOTA project ended, a start has been made in reaching out for a more effective capacity building on geodata usage to enable the tapping of the full potential arising from geodata, which is increasingly becoming available but is under-used. More specifically, our work aims to contribute to the conservation and sustainable use of forests in Eastern Africa. The example of the Kakamgea-Nandi forests can serve here as an example for demonstrating how the spatial reference enables inter- and transdisciplinary approaches and facilitates science to feed into implementations. But as pointed out, geodata processing per se offers far more versatile opportunities for cooperation.

References Athman, J.A. & M.C. Monroe (2001): Elements of effective environmental education programs. In: A. Fedler (Ed.), Defining Best Practices in Boating, Fishing, and Stewardship Education, Recreational Boating and Fishing Foundation, Washington D.C., 37– 48, http://www.d.umn.edu/~kgilbert/educ5165- 731/Readings/Elements%20of%20Effective%20EE.pdf (25 October 2014). Cross-Cutting Issues Track 126/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Farwig, N., T. Lung, G. Schaab & K. Böhning-Gaese (2014): Linking land use scenarios, remote sensing and monitoring to project impact of management decisions. In: Biotropica, 46(3), 357–366 (doi: 10.1111/btp.12105). Hungerford, H.R. & T.L. Volk (1990): Changing learner behavior through environmental education. In: The Journal of Environmental Education, 21(3), 8 –22. Huth, K., N. Mitchell & G. Schaab (2008): Judging and visualising the quality of spatio- temporal data on the Kakamega-Nandi forest area in west Kenya. In: A. Stein, J. Shi & W. Bijker (Eds.), Quality Aspects in Spatial Data Mining, London, Boca-Raton, 297-314. Lübker, T. (2014): Object-based remote sensing for modelling scenarios of rural livelihoods in the highly structured farmland surrounding Kakamega Forest, western Kenya. PhD thesis, Technical University Dresden, Institute for Cartography, http://nbn- resolving.de/urn:nbn:de:bsz:14-qucosa-150628. Lung, T., M.K. Peters, N. Farwig, K. Böhning-Gaese & G. Schaab (2012): Combining long- term land cover time series and field observations for spatially explicit predictions on changes in tropical forest biodiversity. In: International Journal of Remote Sensing, 33(1), 13-40 (doi: 10.1080/01431161.2010.527867). Lung, T. & G. Schaab (2010): A comparative assessment of land cover dynamics of three protected forest areas in tropical eastern Africa. In: Environmental Monitoring and Assessment, 161(1), 531-548 (doi: 10.1007/s10661-009-0766-3). Mitchell, N. (2011): Rainforest change analysis in Eastern Africa: A new multi-sourced, semi- quantitative approach to investigating more than 100 years of forest cover disturbance. PhD thesis, University Bonn, Department of Geography, http://nbn-resolving.de/urn:nbn:de: hbz: 5N-26793. Mitchell, N., G. Schaab & J.W. Wägele (Eds.) (2009): Kakamega Forest ecosystem: An introduction to the natural history and the human context. In: Karlsruher Geowissenschaftliche Schriften (KGS), Reihe A, Bd. 17, ed. by G. Schaab. Pandey, V.C. (2006): Environmental Education. Delhi. Paul, L. & G. Schaab (2015): Developing environmental education tools based on geodata to create awareness for the Kakamega-Nandi Forests ecosystem. In: Kartographische Nachrichten. Journal of Cartography and Geographic Information, 5/2015, 296-303 (http://www.dgfk.net/index.php?do=pub&do2=kna&do3=oa). Schaab, G. (2015): Die Bedeutung von DAAD-Hochschulkooperation für die angewandte Forschung in Entwicklungsländern: Geodatennutzung in Ostafrika. In: Hochschule Karlsruhe – Technik und Wirtschaft, Forschung aktuell 2015, 23-25. Schaab, G. (2007): Capacity development within the BIOTA East Africa project - Promoting the use of spatial information in biodiversity research and management. In: P. Zeil & S. Kienberger (Eds.), Geoinformation for Development. Bridging the Divide through Partnerships, Heidelberg, 44-49. Schaab, G., B. Asser, K. Busch, P. Dammann, N. Ojha & H. Zimmer (2009a): Interaktive Visualisierungen zur Unterstützung von Biodiversitätsforschung und -management in einem Entwicklungsland – Erfahrungen und Herausforderungen. In: Kartographische Nachrichten. Fachzeitschrift für Geoinformation und Visualisierung, 5/2009, 264-272. Schaab, G., B. Khayota, G. Eilu & J.W. Wägele (Eds.) (2010): The BIOTA East Africa atlas. Rainforest change over time. Karlsruhe. Schaab, G., T. Lübker, T. Lung & N. Mitchell (2009b): Remotely sensed data for sustainable biodiversity management. The case model of Kakamega Forest in western Kenya. In: Cross-Cutting Issues Track 127/141 GI-diversity – Taking the activities in the Kakamega-Nandi forests area as example Gertrud Schaab

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Proceedings (digital) of the 33nd International Symposium on Remote Sensing of Environment “Sustaining the Millennium Development Goals”, 4-8 May 2009, Stresa, Lago Maggiore (Italy), ref. 479. Schaab, G., T. Lübker & S. Schwarz (2011): The challenge of targeting different user groups with The BIOTA East Africa Atlas. In: Proceedings (digital) of the 25th International Cartographic Conference (ICC 2011) ‘Enlightened View on Cartography and GIS’, 3-8 July 2011, Paris (France), ID CO-296. Weist, C., K. Busch, N. Mitchell & G. Schaab (2013): Investigating East African Forest Use Histories: a Visualisation Tool for a Combined Text and Geodata Study. http://www.iaf.hs- karlsruhe.de/ gvisr/projects/tools/vis_tool/text/title.htm.

Acknowledgements Special thanks goes to former coworkers (by name Nick Mitchell, Cornelia Weist, Dorothea Heim, Nirmal Ojha), the former student Lisa Paul, project colleagues (including Alex Khaemba and Gerald Eilu) and many more cooperation partners in Kenya (e.g. at MMUST, Nature Kenya, KWS, KFS, NMK) and Uganda (e.g. at Makerere University).

Biographical Notes Gertrud Schaab teaches at Karlsruhe University of Applied Sciences within the Bachelor study programmes on Geo-Information Management and Geodesy & Navigation as well as within the International Geomatics Master programme. Her lectures cover cartography, GIS and remote sensing. These fields she is also following with her research, currently focusing on Eastern Africa.

Contacts Prof. Dr. Gertrud Schaab Karlsruhe University of Applied Sciences Faculty of Information Management and Media Moltkestr. 30 D-76133 Karlsruhe GERMANY Tel.: +49 /(0)721/925-2923 Fax: +49 /(0)721/925-2597 Email: [email protected] Web site: www.iaf.hs-karlsruhe.de/gvisr/

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda

Hitimana Jean Pierre, Rwanda

Key words: GPS, GIS and SDI, environmental restoration, metadata, Geodata, Webmapping, Geo-portal

Abstract In these last recent years farmers in the sector of Maraba in South Province of Rwanda had face challenges to keep producing good quality coffee and to be the 1st place in competition of cup of Excellence. We conducted this research in order to show how the use of Geographic Information Systems (GIS) and Spatial Data Infrastructure (SDI) models as the research method growing and producing good quality coffee in taking into consideration environmental factors like: Elevation and temperature, Rainfall and water supply, Soil, Aspect and slopes.

The findings in this research about the selection of zones of coffee plantation and relation relationship to coffee quality will be published on Geo-Portal where maps and metadata created or collected will be available to the public and particularly to Maraba sector community. The results of this research will be presented to Maraba sector community in a workshop so that they can gain knowledge of the land and the good quality of Maraba coffee.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Introduction In these last recent years farmers in the sector of Maraba in South Province of Rwanda had face challenges to keep producing good quality coffee and to be the 1st place in competition of cup of Excellence. We conducted this research in order to show how the use of Geographic Information Systems (GIS) and Spatial Data Infrastructure (SDI) models as the research method growing and producing good quality coffee in taking into consideration environmental factors like: Elevation and temperature, Rainfall and water supply, Soil, Aspect and slopes.

This research paper focuses on the impact of environment or site selection on coffee quality using GIS and SDI. According to Nebert (2004) … business development, flood mitigation, environmental restoration, community land use assessments and disaster recovery are just a few examples of areas in which decision-makers are benefiting from geographic information, together with the associated infrastructures (i.e. Spatial Data Infrastructure or SDI) that support information discovery, access, and use of this information in the decision-making process.

Methodologies Methodology Using Site Selection (Environment) and GIS Fieldwork According to FAO (2011) growing and producing good quality coffee, several important environmental factors should be taken into account for instance Elevation and Temperature, Rainfall and water supply, Soil, Aspect and slopes and a Fieldwork has been conducted in maraba sector, south province in Rwanda in order to determine those factors (see the picture1below).

Picture1: Discussion with the local Community in localization of the area of research Cross-Cutting Issues Track 130/141 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda Hitimana Jean Pierre

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya The Maraba sector, south province in Rwanda has got 3 Zones of coffee fields and each Zones has got its particular flavor of coffee.

Map1: Maraba sector, South Rwanda

Elevation The influence of geography on the flavor of a coffee bean is profound. All coffee grows in the tropics, but the altitude at which it is grown contributes significantly to a coffee’s taste profile. Mountainous regions of the Coffee Belt, a tropical band extending approximately 30º north and south of the equator, produce the world’s truly great arabica coffees and Rwanda is included in that zone.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Map3: World Map

Central and South America, southern Asia and some Pacific islands, and mid to southern Africa represent the world’s foremost coffee growing regions. High elevations above 3,000 to 6,000 feet and beyond provide ideal growing conditions for the coffee tree: a frost-free climate averaging 70º F year- round, moderate rainfall, and abundant sunshine. These conditions prolong bean development to enhance flavor, brightness, and aroma—the primary factors by which coffee quality is typically evaluated.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Figure1: effect of altitude on coffee flavor

High-grown beans are hard, dense, and possess the potential for concentrated coffee flavor. Central America grades the quality of its coffees on the basis of the altitude at which they are grown. A strictly hard bean (SHB) designation in Guatemala, for example, signifies coffee grown at or above 4,500 feet. Mexico applies the term altura, meaning “high” in Spanish, to identify its high-altitude coffees. Generally, as growing elevation increases, a coffee’s flavor profile becomes more pronounced and distinctive. From the mild and sweet taste qualities of a low-grown Brazilian bean at 2,500 to 4,000 feet to the soaring floral notes of an Ethiopian grown at elevations approaching 6,000 feet, altitude heightens a coffee’s ability to deliver bigger and brighter varietal nuance and complexity.

Low-elevation coffee regions, on the other hand, impose harsher growing conditions on the coffee tree. Higher temperatures and less rainfall cause coffee to ripen more quickly resulting in beans with taste qualities that range from simple and bland to earthy or murky. The bean structure of coffee grown downslope tends to be softer than the hard-bean coffees grown above 4,500 feet. Consequently, these more delicate coffees do not tolerate darker roasts well and suffer from increased flavor loss when stored.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Map4: Map of Altitudinal regions of Rwanda

High elevation improves the quality of the bean and potential cupping quality. Due to a delay in ripening brought about by cooler weather associated with higher altitudes, the inherent characteristics of acidity, aroma and bold bean can develop fully. (Bold bean is classified as being the size between a large and a medium sized bean, with its width/ length ratio bigger than that of a large bean).

Temperature Arabica coffee prefers a cool temperature with an optimum daily temperature of between 20° to 24°C.

Temperatures greater than 30°C cause plant stress leading to a cessation of photosynthesis. Mean temperatures of less than 15°C limit plant growth and are considered sub-optimal. Arabica coffee is frost susceptible. Use of shade trees will reduce the incidence of frost. The average mean temperatures of selected area of Maraba are 18 – 20 degree (see the map below):

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Map5: Temperature map of Rwanda

Rainfall and water supply Ideal rainfall for Arabica coffee is greater than 1200 to 1500 mm per year. Both the total amount and the distribution pattern are important.

The maraba Annual rainfall or precipitation is situated between 1200 to 1400 mm (see the map)

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Map6: Precipitation map of Rwanda

Rain should to be distributed over seven to nine months of the year, as is the case especially at higher elevations. At lower elevations, the dry season is often too pronounced. Lack of rainfall in either amount or timing can be compensated for by using irrigation.

Coffee needs a dry, stress period with little or no rain to induce a uniform flowering. Without a stress period, flowering many extend over many months making harvesting more difficult. Maraba has normally has such a stress period of three to four months of dry weather at elevations of 3000 m.a.s.l. or more.

Coffee requires adequate water during the growing and cropping period, however it also requires a dry stress period followed by sufficient rain or irrigation to promote uniform flowering and a good fruit set. Local community in Maraba has inaf water to irrigate their coffee plantation because of the river Butamu which passes through Maraba. Moreover the local community has huge quantity of ground water that they use to drink and coffee Industry management.

Many plantings suffer from moisture stress at the time of year when they need adequate water for growth and cropping. The local rainfall pattern indicates that supplemental irrigation, especially to induce uniform flowering and good fruit set, would be beneficial. Unless regular rain is received, young trees should be irrigated (or hand watered at least twice a week if irrigation is not available) to ensure establishment of the newly planted trees. Locating coffee plantings near a water supply for possible irrigation as well as for processing of cherry is desirable. Water requirements can be reduced by use of proper, well-established, shade trees, mulch and cover crops. Soil type For successful production, a free draining soil with a minimum depth of one metre is required. Coffee will not tolerate water logging or 'wet feet'. Cross-Cutting Issues Track 136/141 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda Hitimana Jean Pierre

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Coffee can be grown on many different soil types, but the ideal is a fertile, volcanic red earth or a deep, sandy loam. Yellow-brown, high silt soils are less preferred. Avoid heavy clay or poor draining soils. Most soils in maraba loam earths suitable for coffee.

Map7: Rwanda map of texture class

Coffee prefers a soil with pH of 5 to 6. Many cultivated soils of Maraba are acid (less than pH 5) and need lime or dolomite. Few soil test results exist, but indicator plants point to a pH less than 5 with low available phosphorus and thus shortages of many other nutrients. Low pH will limit crop performance by upsetting the availability of key nutrients to coffee plants (see Figure 2). Good management and applications of dolomite or lime can alter and improve soil pH and fertility.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Figure 2. Effect of soil pH on nutrient availability

Slope and aspect (slope % and direction) An easterly or southern facing aspect with a slope less than 15% is preferable. Most locations on the Central Plateau where Maraba is, have a gentle slope and no extra measures are required. Steeper slopes present a major erosion risk and require terracing or special management such as contour furrows or preferably grass strips.

A slight slope will improve air drainage and reduce damage from frost. Do not plant coffee at the bottom of a slope or in shallow dips where cold air can pool, as frost damage is more likely here. Usually it is best not to plant the bottom third of a slope as it will be colder and sometimes waterlogged. Exposed aspects subject to strong winds, should either be avoided

Methodology Using SD The increasing dissemination of geospatial data has the ability to support more informed decisions in a large number of sectors of today’s society including to improve the quality of coffee in Rwanda. The National Spatial Data Infrastructure (NSDI) is defined in the US Presidential Executive Order as ‘the technology, policies, standards and human resources necessary to acquire, process, store, distribute, and improve utilisation of geospatial data’.

We created a model for GeoPortal and this Model helped us to plan and to test the GeoPortal before it creation (see figure3). The Model below has got 3 parts: The input, where remote sensing data, fieldwork and GIS data have inserted into the model. Tools: at this part of the model, data coming from the input are processed by tools like ArcGIS (to create maps), Apache (to publish data), Mysql (to store data) and GeoNetwork for maps and metadata visualization for giving the Output on the GeoPortal.

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11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya A Model for SDI Creation

Figure3: GeoPortal Model

Results: Geoportal Creation To grow and produce good quality coffee and several other plants, the use of spatial data for site selection it is very important. The Maps of Rwanda on rainfall and water supply, Soil type, elevation and temperature, aspect and slope that are shown in this paper, and their metadata they can retrieve on this GePortal: http://gis.ur.ac.rw/

Cross-Cutting Issues Track 139/141 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda Hitimana Jean Pierre

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya

Figure4: Metadata Portal

Conclusion High-altitude coffees generally command a far better market price due to their exceptional flavor and vibrancy, lower yield per coffee tree, and the challenge they pose to coffee farmers in remote mountainous areas who must produce and market the crops. This is not to say that the higher a coffee is grown, the better it is. After all, the quality of any coffee is ultimately determined by an individual’s taste preference. Altitude is but one factor that shapes a coffee’s overall flavor profile. Elevation influences a number of these factors and must be considered along with temperature, rainfall and water supply, soil, slope and aspect when determining where to plant coffee. An elevation greater than 1000 m above sea level (m.a.s.l.) is required for Arabica coffee. Low elevation Arabica coffee does not possess the quality required by the world markets. The areas with 3000 metres are preferred for production of superior quality coffee and Maraba has ample areas of land 3000m m.a.s.l. and above.

References: FAO: http://www.fao.org/docrep/008/ae939e/ae939e03.htm MINAGRI SPATIAL DATABASE http://www.minagri.gov.rw/IMG/jpg/ COFFEE QUALITY ASSESSMENT http://www.fao.org/docrep/008/ae939e/ae939e09.htm#TopOfPage THE EFFECT OF ALTITUDE ON COFFEE FLAVOR http://scribblerscoffee.wordpress.com/2009/12/02/the-influence-of-altitude-on-coffee-flavor/ RESPONSIBLE GEOSPATIAL DATA SHARING: A CANADIAN VIEWPOINT http://www.sdimag.com/20120319657/Responsible-Geospatial-Data-Sharing-A-Canadian- Viewpoint.html

Cross-Cutting Issues Track 140/141 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda Hitimana Jean Pierre

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya SPATIAL DATA INFRASTRUCTURE: INTERNATIONAL SCENARIO http://geospatialworld.net/index.php?option=com_content&view=article&id=19898&Itemid=96

Biographical notes: Hitimana Jean Pierre is a GIS Officer at University Rwanda in the Center of GIS. He holds a Bachelor’s Degree in Computer Engineering and Information Technology from Kigali Institute of Science and Technology in Rwanda. He holds a Master in ICT Policy and Regulation.

He has got 4 Certificates in Cisco Systems (CCNA), one Certificate in Spatial Data Infrastructure (SDI) and He has got skills in Geographic Information Systems. The research interest of Jean Pierre lies on the application of Geographic Information Science and Remote Sensing to development related issues and Spatial Data Infrastructure (SDI), Web Mapping, poverty reduction, land use/land cover mapping and Climate Change analysis, Natural resource management and Sustainable Development. Database Systems, Analysis of Algorithms, Web Technologies, Software Engineering, Networking, Specification and design of Graphical User Interface, Spatial Data Infrastructure (SDI), I gained all those skills and knowledge in the field of Computer Science.

Contacts: Institution: University of Rwanda Address: P.O.Box: 56 Huye-Rwanda Tel: +250781139420 E-mail: [email protected] Website: www.ur.ac.rw www.cgis.ur.ac.rw

Cross-Cutting Issues Track 141/141 Use of GIS and SDI in Promoting Coffee Quality in Maraba Sector, South Province of Rwanda Hitimana Jean Pierre

11th Esri Eastern Africa User Conference (EAUC) 2 – 4 November 2016 | Acacia Premier Hotel, Kisumu, Kenya Esri Eastern Africa 3rd Floor KUSCCO Center, Upper Hill P.O.Box 57783-00200, Nairobi, Kenya Tel: 254-20) 2713630/1/2, (0) 722 521341 Email: [email protected]