Kenya is at the forefront of a socioeconomic transformation, aiming to turn into an industrialized middle income country by 2030. Kenya Vision 2030 has identified energy as a key foundation and one of the infrastructural “enablers” upon which the economic, social and political pillars of this long-term development strategy will be built. Predicting the future of energy systems however, involves risks due to various uncertainties. Therefore, systematic energy planning at national and sub-national/County level is highly recommended through the adoption of more realistic assumptions on the future evolution and profile of demand and robust pre-feasibility of prospective projects including the integration of renewable energy sources, which the country is endowed with. This thesis provides a comprehensive analysis of the energy sector for Kakamega County in Western Kenya. The current energy demand level was estimated for six selected sectors of the County namely Residential, Industrial, Transportation, Commercial, Public and Agricultural. Additionally, the renewable energy resources potential was assessed at local level using GIS and other available data. LEAP software was used in order to model and project the energy demand and supply based on three 15-year scenarios till 2030, developed to support the economic, social and environmental sustainability of the County. This study intended to create a framework aiming to facilitate sub-national energy planning in developing countries and it is expected that the findings will be complementary to already existing energy planning models but also the base for future research towards energy poverty elimination.

Abstract in English, Venda and Tsonga / Released prisoners in Kenya have a 75% likelihood of committing another crime and a 50% probability of going to jail two years after their discharge from prison custody. From the trend of recidivism in Kenya, there are a staggeringly high number of offenders being incarcerated and eventually released back to the community, and the high risk of re-arrest and reincarceration is a concern for policymakers, criminologists and correctional managers. This study examined the influence of offender characteristics, offender reintegration and community perception and attitude regarding recidivism in Kakamega County, Kenya. The study adopted a survey research design. Findings reveal a statistically significant relationship between offender characteristics and recidivism. In addition, offender reintegration and community perception and attitude towards offenders greatly influence recidivism. From the study, it is recommended that the government provide correctional officers with the required resources to use the actuarial risk assessment model. The model is applied to the released offenders to predict the future probability of recidivism. In addition, it is recommended that the government and the various correctional stakeholders come up with an integrated approach that specifically targets successful re-entry of offenders upon release from prison. Finally, it is recommended that the government develop programmes targeting awareness of the community members to desist from stigmatising ex-offenders. / Vhafariwa vho vhofhololwaho ngei Kenya vha na khonadzeo ya 75% ya u ita vhuṅwe vhutshinyi na 50% ya khonadzeo ya u ya dzhele miṅwaha mivhili nga murahu ha u bva tshiṱokisini. U bva kha nzulele ya u tshinya fhafhu ngei Kenya, hu na u mangadza huhulwane ha tshivhalo tshi re nṱha tsha vhatshinyi vha re dzhele vhane vha fhedzisela vho vhofhololelwa murahu kha tshitshavha, khohakhombo khulwane ya u dovha u farwa hafhu na u valelwa hafhu dzhele zwi vhilaedzisa vhabveledzi vha mbekanyamaitele, vhaḓivhi vha zwa vhutshinyi na vhalanguli vha vhululamisi. Ngudo i ṱola ṱhuṱhuwedzo ya zwiṱaluli zwa mutshinyi, mbuedzedzo ya mutshinyi na zwine tshitshavha tsha mudzhiisa zwone na vhuvha zwi tshi ya kha u tshinya hafhu kha Dzingu ḽa Kakamega, Kenya. Ngudo yo shumisa tsedzuluso ya pulane yo dzudzanywaho ya ṱhoḓisiso. Mawanwa o dzumbulula tshivhalo tsha vhushaka ha ndeme vhukati ha zwiṱaluli zwa mutshinyi na u tshinya hafhu. U ḓadzisa khazwenezwo, mbuedzedzo y mutshinyi na zwine tshitshavha tsha mudzhiisa zwone na vhuvha zwi tshi ya kha vhatshinyi zwi ṱuṱuwedza nga huhulu u tshinya hafhu. U bva kha ngudo, hu themendelwa uri muvhuso u ṋetshedze vhaofisiri vha ndulamiso zwiko zwine zwa ṱoḓea u shumisa tshiedziso tsha u ṱola khohakhombo tsha vhukuma. Tshiedziso tshi shumiswa u vhofholola vhafariwa u humbulela khonadzeo ya vhumatshelo ya u tshinyahafhu. U ḓadzisa kha zwenezwo, hu themendelwa uri muvhuso na vhadzhiamukovhe vho fhambanaho vha vhululamisi vha ḓe na kuitele kwo ṱanganelaho kwo livhiswaho tshoṱhe kha u dzhena hafhu ha vhatshinyi musi vha tshi tou bva dzhele. Tsha u fhedzisela, hu themendelwa uri muvhuso u bveledzise mbekanyamushumo dzo livhiswaho kha u tsivhudza miraḓo ya tshitshavha u sa i sa phanḓa na u fara vhatshinyi vha kale nga nḓila i si yavhuḓi. / Vakhotsiwa lava tshunxiwaka eKenya va na 75% wa ntolovelo wa leswo va nga endla vugevenga byin’wana na 50% ta nkoteko wa ku ya ejele nakambe endzhaku ka ku tshunxiwa ka vona ejele. Kusuka eka ntolovelo wa ku vuyelela ku endla vugevenga nakambe eKenya, ku na nhlayo ya le henhla hindlela yo hlamarisa ya vaonhi lava va nga eku pfaleriweni ekhotsweni naswona endzhaku ka swona va tshunxiwa ku vuyela eka tindhawu ta vaaki, naswona ku na nxungeto wa le henhla wa ku khomiwa nakambe na ku pfaleriwa ekhotsweni nakambe hi vuntshwa, leswi i xivileriso eka vaendlatipholisi, vativi hi swa vugevenga na vafambisi va makhotso. Ndzavisisadyondzo lowu wu kambele nhlohlotelo wa swihlawulekisi swa vaonhi, ku hlanganisa nakambe vaonhi na vanhu eka tindhawu ta vaakandhawu na mavonelo na maehleketelo ya vaakandhawu hi mayelana na ku vuyelela ka swigevenga ku endla vugevenga eka Xifundza xa Kakamega, eKenya. Ndzavisisadyondzo lowu wu tirhise dizayini ya ndzavisiso wa mbalango ku nga survey research design. Leswi kumiweke swi paluxe vuxaka bya le henhla hindlela ya tinhlayonhlayo exikarhi ka swihlawulekisi swa vaonhi na vuyelelo bya ku endla vugevenga nakambe. Na le henhla ka sweswo, ku hlanganisa hi vuntshwa vaonhi na vaakandhawu nakambe hi vuntshwa na mavonelo na maehleketelo ya vaakandhawu eka vaonhi swi hlohlotela swinene vuyelelo bya ku endla vugevenga nakambe. Kusuka eka ndzavisisadyondzo, ku bumabumeriwa leswaku mfumo wu nyika vaofisiri va makhotso swipfuno leswi lavekaka ku tirhisa modlolo wa nhlahluvo wa nxungeto wa xiakichuwari ku nga actuarial risk assessment model. Modlolo lowu wu tirhisiwa eka vaonhi lava tshunxiweke ku vhumba nkoteko wa nkarhi lowu taka wa vuyelelo bya vugevenga nakambe. Ku tlhela nakambe ku bumabumeriwa leswaku mfumo na vakhomaxiave va makhotso vo hambanahambana va va na endlelo leri hlanganisiweke leri kongomisiwaka ngopfungopfu ku humeleka kahle ka ku vuyela ka vaonhi eka tindhawu ta vaaki loko vaonhi va tshunxiwa ekhotsweni. Xo hetelela, ku bumabumeriwa leswaku mfumo wu endla minongonoko leyi kongomisiweke eka vulemukisi bya vaakandhawu leswaku va tshika ku nyenyemuka khale ka vaonhi lava a va khotsiwile. / Corrections Management / Ph. D. (Criminal Justice)

Despite 25 years of concerted efforts by African governments to adopt consistent policies for increasing food production, hunger and poverty are still prevalent in the continent. Using Bernanke's conceptualization of the credit channel theory of monetary policy, the purpose of this correlational study was to investigate whether a subsidy program, the National Accelerated Agricultural Inputs Access Program (NAAIAP), affected the rates of fertilizer usage and food production in Kakamega County, Western Kenya. Purposive stratified sampling was used to select 114 participants consisting of 72 farmers in each of the 2 groups: NAAIAP beneficiaries and nonbeneficiaries. Participants completed a survey on fertilizer usage rates, income earned, and surplus maize yield. Data were analyzed using multiple regression to test whether there was a difference between the beneficiary and nonbeneficiary groups regarding income, surplus product, and the dependent variable of fertilizer usage. Results indicated that beneficiaries of NAAIAP credit program bought and prepared to use fertilizers significantly earlier than did their counterparts. Further, the results of multiple regression indicated significant positive correlation (p <.05) between income earned from sale of surplus maize yield and quantity of fertilizer used by farmers in Kakamega County. These findings suggest that NAAIAP improved food security and farmers' income in Kakamega Count. This study contributes to social change by recommending to subsidy program administrators in Kakamega County to consider policy changes. Such policy changes may improve program outreach to resource-poor farmers and improve income and product yield in the agricultural sector of Kenya.

This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research. / Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen.

This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion / Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion

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