Doctoral Degrees (Geography)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6615

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Landscape ecology of coffee pests in smallholdings: influence of landscape fragmentation, farming systems and a warming climate in Murang’a County, Kenya.
(2022) Mosomtai, Gladys Jebiwot.; Odindi, John Odhiambo.; David, Guillaume.; Abdel-Rahman, Elfatih Mohamed.; Mutanga, Onisimo.
Coffee production systems have resulted in simplified landscapes with fragments of natural and semi-natural vegetation characterised by loss of biodiversity, high pests and disease incidences and excessive pesticide input. Consequently, the resilience of coffee landscapes against climate change impacts such as high diurnal temperature range, erratic rains, and prolonged droughts is weakened. Equally, controlling pests and diseases using natural enemies is no longer effective due to the unselective use of harmful chemicals. The present study aimed to understand the role of landscape ecology in a typical smallholder coffee-based landscape in creating suitable ecological conditions for the proliferation of coffee pests, specifically, coffee berry borer (CBB), Hypothenemus hampei, and the Antestia bugs Antestiopsis thunbergii (ABT) and A. facetoides (ABF) in an important coffee growing zone in central Kenya. The study also examined the impact of limiting temperature rise to below 2oC on habitat suitability for growing Arabica coffee to guide the implementation of the Paris agreement, which requires countries to stabilize the global mean surface temperature rise to below 1.5oC and in the worstcase scenario, well below 2.0oC above the pre-industrial levels. Firstly, the study explored Sentinel 2, Landsat 8 and PlanetScope datasets to characterise the smallholder coffee-based landscape and the level of fragmentation in each agro-ecological sub-zones of the upper midland (UM) agro-ecological zone. Sentinel 2 provides a robust dataset for land use and land cover (LULC) classification, with shortwave near-infrared and green bands being critical for classifying coffee bushes. Coffee was the dominant cover type in the higher agro-ecological sub-zones of Kenya, whereas annual crops dominated the lower sub-zones. Secondly, the study sought to identify the significant spatial scale and landscape structure that influenced the abundance of the three coffee pests, given that CBB had a low dispersal capacity and vice versa for the antestia bugs. The results showed that the pests foraged within a radius of 300m, with CBB having the shortest optimum foraging distance of 100m. The CBB abundance was strongly influenced by contiguous coffee patches, especially at higher elevations, whereas adjacent patches were more suitable for antestia bugs, especially cropland in the lower agroecological sub-zones. Thirdly, the shade and edge effect on microclimate and coffee pest abundance were examined. Generally, CBB preferred shaded coffee in the lower sub-zones and full-sun coffee in the higher sub-zones. For Antestia bugs, ABT preferred shaded coffee in all the agro-ecological sub-zones, whereas ABF preferred full-sun coffee, especially in the low sub-zones. Notable also was the influence of the edge effect of agroforest in lowering the mean temperature of full-sun coffee plots. Finally, the study looked at the impact of limiting v temperature rise to below 2oC under the Representative Concentration Pathways (RCP) 2.6 scenario on habitat suitability for growing Arabica coffee. The results showed that the area under coffee will increase, especially in 2070, and the coffee suitable range will shift to lower sub-zones. Overall, the study revealed that the existing landscape structure in smallholder coffee agrosystems favours coffee pests proliferation. Pest pressure at the lower sub-zones is high, especially in coffee plots without shade. However, implementing climate-friendly policies will reverse the current trend, making the lower sub-zones more suitable for growing Arabica coffee. An increase in acreage for planting coffee will translate to more yields, which could alleviate poverty and grow Kenya’s gross domestic product. The study underscores the urgency for smallholder farmers to shift their coffee production systems to climate-smart options such as increasing shade in their plots. This will increase their landscape resilience against climate change and pest control. Additionally, policy makers need to implement climate policies and promote clean energy development to limit temperature rise by the end of the century.
Long-term and climatological studies on sulphur dioxide (SO²) using ground based and space-borne measurements over South Africa.
(2018) Venkataraman, Sangeetha.; Gebreslasie, Michael Teweldemedhin.; Wright, Caradee Yale.
Abstract is available in the PDF file.
Quantifying ecosystem services within a reforested urban landscape using remote sensing in eThekwini region of KwaZulu-Natal, South Africa.
(2022) Mngadi, Mthembeni.; Odindi, John Odhiambo.; Mutanga, Onisimo.
Abstract available in PDF.
Transformational adaptation: the community ecosystems-based adaptation assemblage in KwaZulu-Natal, South Africa = Enokuxhumana: Inhlanganisela Yokuxhumana Komphakathi Okugxile Esimeni Sempilandawonye KwaZulu-Natali,eNingizimu Afrika.
(2021) Ramanand, Sarisha.; Nel, Adrian.
Climate change poses a fundamental global threat to society, especially for those who depend directly on natural ecosystems for their survival and sustainable livelihoods. The lack of research on climate adaptation interventions was identified by the 2019 National Adaptation Strategy of South Africa as a stumbling block to climate adaptation. This thesis investigates and tracks the emergence, evolution and scaling up of a Community Ecosystems-Based Adaptation (CEBA) intervention that is operated by Wildlands, an NGO in KwaZulu-Natal, as a local response to the current climate adaptation deficit. My original contribution is the application of an assemblage approach that characterises an integrated CBA-EBA adaptation intervention (Wildlands CEBA Assemblage) as an adaptation assemblage, and to build on the established knowledge of Transformational Adaptation, which is the primary theoretical underpinning of this research. The four study objectives are as follows: 1) to understand the complex range of factors that have influenced the mainstreaming of the Wildlands CEBA Assemblage and a marginalised (adaptation) agenda; 2) to explore the upscaling of the Wildlands CEBA Assemblage; 3) to explore the impacts of the Wildlands CEBA Assemblage on the livelihoods of participating communities in KwaZulu-Natal and 4) to explore the utility of an assemblage approach to understanding adaptation. The thesis embraces a practical approach for advancing knowledge on Transformational Adaptation by engaging with aspects of poverty reduction through livelihood diversification, as well as the challenges associated with the ambiguities and uncertainties. To achieve the research aims, a multiple case study design and a pragmatic and interpretive approach were adopted by using the mixed methods research technique. Interviews for the main study subsequently commenced with 29 key informants and 157 participating community members across seven sites, using a semi-structured interview guide. Thematic and inductive analyses were used to generate data that spoke to the organisational development, poverty reduction and individual capability themes within the research. Furthermore, I developed a CEBA Analysis Framework that focused on analysing and interpreting the research findings by drawing on the theories of assemblage thinking and transformation, guided by the supplementary theories of discourse analysis, managerial roles, sustainable livelihoods and individual capabilities. The assemblage approach is a key contribution to this thesis through which interconnected parts of an adaptation intervention can be investigated. Characterising the Wildlands CEBA intervention as an assemblage brings into perspective how it can spread over time and space, by territorialising different geographical landscapes and communities. In addition, the CEBA Analysis Framework made it possible to assess additional aspects. The discursive dimension of the study shows that changes in climate discourses have influenced the evolution of the Wildlands CEBA Assemblage, by expanding the definition and interpretation of the concept of ‘adaptation’. The results pertaining to the ‘enviropreneurship’ livelihood support mechanism within CEBA revealed an increased awareness of climate change, the potential to reduce poverty by direct monetary gain and the diversification of livelihoods through barter and trade mechanisms within the Wildtrust programme suite. However, the implementation of CEBA was not without some confusing and demoralising effects on the communities. A lack of transparency, communication, capacity building, monitoring and evaluation were overshadowed by other organisational and donor priorities, which enhanced the challenges of achieving transformational adaptation for systemic change. Ambiguity and uncertainty were present in the Wildlands CEBA Assemblage, where varying interpretations of ‘CEBA’ negatively impacted the workforce while daily operational work was undertaken; in many cases, this caused confusion and conflict amongst the participating community members. Overall, the Wildlands CEBA Assemblage was rhizomatic in nature as it expanded across political and geographical boundaries, revealing that upscaling climate change adaptation interventions at a landscape level was indeed possible by employing an integrated CBA-EBA approach. While challenges, changes and ‘reassembling’ occurred, the assemblage remained intact. This thesis contributes to the new ‘Transformational Adaptation’ school of thought by being one of the first studies in South Africa to apply an assemblage approach to a landscapelevel climate change adaptation intervention. The thesis suggests that adaptation studies should not only involve a ‘birds-eye view’ of the adaptation intervention (the whole system) in its entirety, but that it is equally important to scrutinise, explore and investigate the actors, discourses, practices, governance regimes, technologies (the ‘moving parts’ of the system) and incentives that influence the system itself. IQOQA Ukungabi bikho kocwaningo mayelana nokungenelela ekuxhumaneni kwesimozulu ngokwenkathi kwakhonjwa yiNational Strategy yaseNingizimu Afrika yowezi-2019 njengesithiyo sokuxhumana kwesimozulu ngokwenkathi. Le thisisi iphenya iphinde ilandele ukuvela, ukuguquka nokukhula Kokuxhumana Komphakathi Okugxile Esimeni Sempilandawonye (Community Ecosystems- Based Adaptation - CEBA) ukungenelela okwakwenziwa yiNhlngango Engenzi Nzuzo (NGO) eWildlands, KwaZulu-Natali, njengempendulo yendawo esimweni esikhona sokwesweleka kokuxhumana kwesimozulu ngokwenkathi. Kwabe sekuqhubeka izimposambuzo zocwaningo olumqoka nababambiqhaza abaqavile abangama-29 namalungu omphakathi ayebambe iqhaza angama-157 ezindaweni eziyisikhombisa zaKwaZulu-Natali, kusetshenziswa isiqondiso semposambuzo esakuhleleka. Ithisisi yathatha indlela eyinhlanganisela ukutshengisa ukungenelela (Wildlands CEBA Assemblage) njengendlela yokuxhumana kwenhlanganisela; uhlelo (oluphelele) oluhlanganisa imisuka eminingi eyinhlanganisela (izingxenyana zohlelo). Le ndlela yavumela ukuphenya izingxenyana ezixhumene zokungenelela kokuxhumana njengoba yasabalala ngokwesikhathi nangokwendawo, igwamandela imimo yezindawo nemiphakathi. Ubukhulu nobubanzi bocwaningo bukhombisa izinguquko ezingxoxweni zesimozulu senkathi, kwaba nomthelela ekukhuleni kweWildlands CEBA Assemblage, ngokukhulisa incazelo yokuhunyushwa komqondomsuka ‘wokuxhumanisa’. Le ndlela yaphinda yagqamisa imithelela yokuncishiswa kobubha ngokwahlukanisa izindlela zokuziphilisa kuyona, nanezindaba ezihambisana nongabazane nokungacaci okwavela ohlelokwenzeni. Naphezu kokuthi ukufakwa kweCEBA kwakungaphuthwe zinselelo, imiphumela yaveza ukuqonda okukhulile kwesimozulu senkathi, nokungenziwa ekwehliseni ububha ngokukwahlukanisa izindlela zokuziphilisa nempilandawonye ethuthukile. Le thisisi inikelela esimeni somcabango ‘Sokuxhumana Kokwenguquko’ ngokuba ngolunye locwaningo lokuqala eNingizimu Afrika ukusebenzisa indlela eyinhlanganisela ekuxhumaneni nasekungeneleleni ekuguqukeni kwesimo sezulu ngokwenkathi ezingeni lendawo. Ithisisi iphakamisa ukuthi izifundo zokuxhumanisa kufanele zingagcini nje ngokufaka ‘ukubuka ngeso lenyoni’ ukungenelela kokuxhumana ngokuphelele, kodwa kuphinda kube semqoka ukuxilonga, ukuhlola nokuphenya abadlali, izingxoxo, okwenziwayo, izinhlaka zokuphatha, ubuchwepheshe nezikhuthazi okunomthelela ohlelweni uqobo lwalo.
Use of geospatial techniques to improve bee farming and bee health across four main agroecological zones in Kenya.
(2023) Makori, David Masereti.; Mutanga, Onisimo.; Odindi, John Odhiambo.; Abdel-Rahman, Elfatih Mohamed.
Amid augmented climate change and anthropogenic influence on natural environments and agricultural systems, the global socioeconomic and environmental value of bees is undisputed. Bee products such as honey, pollen, nectar, royal jelly and to a lesser extent bee venom are important supplemental sources of income generation especially in the underdeveloped rural African areas. Moreover, bee farming is an important incentive for forest conservation, biodiversity and ecosystem services in terms of pollination services. Bee pollination services play a vital role in crop production, hence directly contribute to food and nutritional security for African smallholder farmers. Nevertheless, bee farming and bee health in general are under threat from climate change, agricultural intensification and associated habitat alteration, agrochemicals intensification, bee pests and diseases. Therefore, there is need to establish spatial distribution of bees, their food substrates, floral cycle and biotic and abiotic threats, especially bee pests. Bee pests devastate bee colonies through physical injury and as vectors of pathogens, hence causing a considerable reduction in bee colony productivity. Thus, this study sought to establish geospatial techniques that could be used to improve bee farming and bee health in Kenya. Firstly, this study aimed to determine the spatial and temporal distribution of stingless bees in Kenya using six machine learning ecological niche approaches and non-conflating variables from both bioclimatic, vegetation phenology and topographic features. All machine learning algorithms used herein performed at an ‘excellent’ level with a true skills statistics (TSS) score of up to 0.91. Secondly, the study assessed the suitability of resampled multispectral data for mapping melliferous (flowering plants that produce substance used by bees to produce honey) plants in Kenya. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors’ (i.e., WorldView-2, RapidEye, Spot- 6 and Sentinel-2) spatial and spectral resolutions, and a RF classifier were used to map melliferous plants. Melliferous plants were successfully mapped with up to 93.33% overall accuracy using WorldView-2. Furthermore, the study predicted the distribution of four main bee pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) in Kenya using the maximum entropy (MaxEnt) model and random forest (RF) classifier. The effect of seasonality on the abundance of bee pests was apparent, as indicated by the Wilcoxon rank sum test, with up to 6.35 times more pests in the wet than the dry season. Furthermore, bioclimatic variables especially precipitation contributed the most (up to 77.8%) to all bee pest predictions, while vegetation phenology provided vital information needed to sharpen the prediction models at grain level due to their higher spatial resolution and seasonal and phenological features. Moreover, topography had a moderate influence (14.3%) on the distribution of bee pests. Also, there was a positive correlation between bee pests’ abundance, habitat suitability and high altitude. Anthropogenic influence (as depicted by human footprint data) on the distribution of bee pests was relatively low (1.2%) due to the availability of a variety of bee food substrate from the mixed land use/land cover (LULC) classes, especially farmlands. Using the Pearson correlation coefficient, the prediction models for all bee pests scored at an excellent level (0.84), except for the G. mellonella prediction model, which was ranked ‘fair’ (0.55). Due to the relatively high accuracy for models developed herein to map stingless bees’ distribution, melliferous plants and bee pests’ occurrence and abundance, this study concluded that the models developed could reliably be used to indicate high suitability areas for bee farming. They could also be used to predict high bee pests risk areas for mitigation and management purposes, hence improving bee health and hive productivity.

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Browsing Doctoral Degrees (Geography) by Subject "Climate change."