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.
The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharun spp. hybrid)
(2010) Abdel-Rahman, Elfatih Mohamed.; Ahmed, Fethi B.; Van den Berg, Maurits.
South Africa is the leading producer of sugarcane in Africa and one of the largest sugarcane producers in the world. Sugarcane is grown under a wide range of climatic, agronomic, and socio-economic conditions in the country. Stress factors such as water and nutrient deficiencies, and insect pests and diseases are among the most important factors affecting sugarcane production in the country. Monitoring of stress in sugarcane is therefore essential for assessing the consequences on yield and for taking action of their mitigation. The prediction of sugarcane yield, on the other hand is also a significant practice for making informed decisions for effective and sound crop planning and management efforts regarding e.g., milling schedules, marketing, pricing, and cash flows. In South Africa, the detection of stress factors such as nitrogen (N) deficiency and sugarcane thrips (Fulmekiola serrata Kobus) damage and infestation are made using traditional direct methods whereby leaf samples are collected from sugarcane fields and the appropriate laboratory analysis is then performed. These methods are regarded as being time-consuming, labour-intensive, costly, and can be biased as often they are not uniformly applied across sugarcane growing areas in the country. In this regard, the development of systematically organised geo-and time-referenced accurate methods that can detect sugarcane stress factors and predict yields are required. Remote sensing offers near-real-time, potentially inexpensive, quick and repetitive data that could be used for sugarcane monitoring. Processing techniques of such data have recently witnessed more development leading to more effective extraction of information. In this study the aim was to explore the potential use of remote sensing to quantify stress in and predict yield of sugarcane in South Africa. In the first part of this study, the potential use of hyperspectral remote sensing (i.e. with information on many, very fine, contiguous spectral bands) in estimating sugarcane leaf N concentration was examined. The results showed that sugarcane leaf N can be predicted at high accuracy using spectral data collected using a handheld spectroradiometer (ASD) under controlled laboratory and natural field conditions. These positive results prompted the need to test the use of canopy level hyperspectral data in predicting sugarcane leaf N concentration. Using narrow NDVI-based vegetation indices calculated from Hyperion data, sugarcane leaf N concentration could reliably be estimated. In the second part of this study, the focus was on whether leaf level hyperspectral data could detect sugarcane thrips damage and predict the incidence of the insect. The results indicated that specific wavelengths located in the visible region of the electromagnetic spectrum have the highest possibility of detecting sugarcane thrips damage. Thrips counts could also adequately be predicted for younger sugarcane crops (4–5 months). In the final part of this study, the ability of vegetation indices derived from multispectral data (Landsat TM and ETM+) in predicting sugarcane yield was investigated. The results demonstrated that sugarcane yield can be modelled with relatively small error, using a non-linear random forest regression algorithm. Overall, the study has demonstrated the potential of remote sensing techniques to quantify stress in and predict yield of sugarcane. However, it was found that models for detecting a stress factor or predicting yield in sugarcane vary depending on age group, variety, season of sampling, conditions at which spectral data are collected (controlled laboratory or natural field conditions), level at which remotely-sensed data are captured (leaf or canopy levels), and irrigation conditions. The study was conducted in only one study area (the Umfolozi mill supply area) and very few varieties (N12, N19, and NCo 376) were tested. For practical and operational use of remote sensing in sugarcane monitoring, the development of an optimum universal model for detecting factors of stress and predicting yield of sugarcane, therefore, still remains a challenging task. It is recommended that models developed in this study should be tested – or further elaborated – in other South African sugarcane producing areas with growing conditions similar to those under which the predictive models have been developed. Monitoring of sugarcane thrips should also be evaluated using remotely-sensed data at canopy level; and the ability of multispectral sensors other than Landsat TM and ETM+ should be tested for sugarcane yield prediction.
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 Author "Abdel-Rahman, Elfatih Mohamed."