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Using fishers’ local ecological knowledge for management of small-scale fisheries in data-poor regions: Comparing seasonal interview and field observation records in East Africa
Get PDFThis research article was published in Fisheries Research Journal, Volume 264, 2023.Fishers, scientists and policy makers need to describe, understand and “agree on” variations in fish catches caused by exploitation and climate change for effective fisheries management. To achieve this, relevant data with sufficient spatiotemporal resolution is a necessity. In regions of the Global South, such as the Western Indian Ocean (WIO), fish catch data useful for management is scarce or non-existing. Still, the potential of local ecological knowledge to provide such information has not been fully utilised in these regions. In this study, we evaluated fishers’ local ecological knowledge (based on interviews) against detailed seasonal fish catch variability data based on catch per unit effort (CPUE) records. Because of the importance of the monsoon seasons for marine resource variability, differences in fish catches during the northeast (NE) and southeast (SE) monsoon seasons were investigated. Fishers’ perceptions generally agreed with catch data records, both showing that the NE monsoon season generally provides higher catch rates than the SE monsoon season. The fishers’ perceptions at two of the landing sites (Nyamisati and Shangani) contradict the recorded observations by showing highest fish catches during the SE monsoon season. It was clear, however, that fishers’ perceptions in these two sites focused on the most valuable target species (prawn and tuna in Nyamisati and Shangani, respectively) rather than total catches. In this particular case, fishers’ perceptions facilitated the significance of taking target species into consideration. The findings of this study highlight the importance of integrating local ecological knowledge into scientific research to help understand the complex dynamics of coastal fisheries and improve the management of data-poor fisheries
Use of 210Pb and 137Cs radionuclides as simple method of estimating sedimentation rates on reservoir
Get PDFThis research article was published in Arab Journal of Nuclear Sciences and Applications, Volume 56, 2023.Reservoirs are important for flood control, production of hydroelectric power, fishing, water harvesting and storage. Climate is changing, population and anthropogenic activities are increasing which increase sedimentation rate to most of reservoirs reducing their capacity. With climate change in mind water are becoming scarce resources which need proper storage when available and suitable flood control in areas with unpredicted rainfall. Furthermore, energy demand is increasing with population growth suggesting a need of sustainable management of dams so as to control flood, produce more hydroelectric power and store water to use in dry season. Sedimentation is one of the factors influencing storage capacity of the reservoir. Understanding sedimentation rate, source of sediment and carryout proper management measures is one among of the important methods to preserve dams. 137Cs and 210Pb are important radionuclides for investigating sedimentation rate. The method has been used for more than two decades in developed countries with less use in developing countries. In this study a sediment core was sampled at the centre of Nyumba ya Mungu reservoir in Tanzania, the samples were then transported to China for processing and analysis. High-purity Germanium detector was used for analysis. The results showed that rate of sedimentation in the reservoir ranged from 0.21 to 0.65 cm/year with more sediment coming from Kikuletwa than Ruvu tributary. The range is comparable to most of reservoir in tropics of developing country. Proper utilization of this method can bring sustainable management of water, save communities from flood disaster and increase production of electricity
Indicative Factors for SACCOs Failure in Tanzania
Get PDFA research title was published by Engineering, Technology & Applied Science Research Volume: 13 | Issue: 4,| August 2023SACCOs are viewed as a feasible opportunity toward financial inclusion in an economy where most of the citizens are poor, as they are very essential for the socio-economic development of members, the community, and the world at large. However, SACCOs sometimes do not realize the expected socio-economic potential, especially when they fail. This study aimed to comprehensively assess financial and non-financial factors, at institutional and personal levels, that contribute to the failure of SACCOS in Tanzania. The data were collected using a questionnaire on 5,000 members of SACCOs, obtained using stratified random sampling. Data collected were analyzed using descriptive statistics and binary logistic regression. The findings showed that both financial and non-financial factors, at personal and institutional levels, had a statistically significant and positive relationship with the failure of SACCOs. Therefore, the performance of SACCOs and other Microfinance Financial Institutions (MFIs) should be addressed from a comprehensive view of both financial and non-financial factors, at personal or institutional levels. In other words, the failure of MFIs should be addressed from a holistic point of view
Characterization of phenotypic traits associated with anthracnose resistance in selected common bean (Phaseolus vulgaris L.) breeding material
Get PDFThis research article was published in the research square, 2023Anthracnose caused by Colletotrichum lindemuthianum is the major common bean disease worldwide. This study aimed to determine phenotypic traits associated with anthracnose resistance for future use in breeding programmes. Twenty-two common bean varieties (CBVs) were planted in anthracnose hotspot fields and the same CBVs were planted in a screen house to validate resistance to anthracnose. Anthracnose infection score, leaf length, leaf width, length of fifth internode, length of petiole, plant vigour, canopy height and canopy width were recorded. Data on (i) number of plants emerging; (ii) days to flowering; (iii) days to maturity; (iv) plant stands at harvest; and (v) grain yield were also collected and analysed using R and GenStat software.
Phenotypic traits evaluated differed significantly among genotypes, environment and genotype by environment interaction. Seventy-five per cent of phenotypic traits evaluated were positively correlated to anthracnose resistance. Highly-strong correlations were observed on number of days to maturity, plant stands at harvest, plant vigour and grain yield. Leaf length, leaf width, length of fifth internode, length of petiole and number of stands emerging were strongly correlated to anthracnose resistance. Based on study results, four traits – plant vigour, number of days to maturity, number of plant stands at harvest and grain yield – are recommended for selecting anthracnose-resistant varieties. NUA 48, NUA 64 and RWR 2154 were superior varieties, resistant to anthracnose and high yielding, while Sweet Violet and VTT 923-23-10 were most stable varieties across environments. Further on-farm research is suggested to assess their performance and identify traits preferred by farmers
Short-term forecast techniques for energy management systems in microgrid applications
Get PDFA Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the
global average temperature rise to less than 2°C. Achieving the set targets involves increasing
energy efficiency and embracing cleaner energy solutions. Although advances in computing
and Internet of Things (IoT) technologies have been made, there is limited scientific research
work in this arena that tackles the challenges of implementing low-cost IoT-based Energy
Management System (EMS) with energy forecast and user engagement for adoption by a
layman both in off-grid or microgrid tied to a weak grid.
This study proposes an EMS approach for short-term forecast and monitoring for hybrid
microgrids in emerging countries. This is done by addressing typical submodules of EMS
namely: load forecast, blackout forecast, and energy monitoring module. A short-term load
forecast model framework consisting of a hybrid feature selection and prediction model was
developed. Prediction error performance evaluation of the developed model was done by
varying input predictors and using the principal subset features to perform supervised training
of 20 different conventional prediction models and their hybrid variants. The proposed
principal k-features subset union approach registered low error performance values than
standard feature selection methods when it was used with the ‘linear Support Vector Machine
(SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion
of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance
than the individual regression models with a reduction in Mean Absolute Error (MAE) by
5.4%.
In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and
Random Forest (RF) model for short-term power outage prediction was proposed that predicted
accurately almost half of the blackouts (49.16%), thereby performing slightly better than the
stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart
meter was developed to realize the implemented energy forecast and offer user engagement
through monitoring and control of the microgrid towards the goal of increasing energy
efficiency
Modeling the transmission dynamics and control of cassava mosaic disease with non-cassava host plants
Get PDFA Dissertation Submitted in Partial Fulfilment of the Requirements for the Master’s Degree in Mathematical and Computer Sciences and Engineering of the Nelson Mandela African Institution of Science and TechnologyFor many years cassava mosaic disease hinders cassava production in Africa. In this study,
the mathematical model for the transmission dynamics and control of cassava mosaic disease
in cassava and non-cassava host plant populations was formulated and analysed. The next generation matrix technique was employed to obtain the basic reproduction number (R0). The
local stability of the disease-free equilibrium point was determined using the Linearization
method while the normalized forward sensitivity index technique was utilized to analyse the
sensitivity of the model parameters. The optimality control technique, based on the Pontraygin
Maximum Principle, with roguing activities and insecticide application as control strategies,
has been applied to achieve the optimality of objective function. Moreover, the Incremental
Cost-effectiveness Ratio approach was used to perform a cost-effectiveness analysis on control
strategies in combating cassava mosaic disease. Lastly, the numerical simulation for the formu lated models to assess sensitive parameters, global stability and the optimal solution was per formed. The findings reveal the existence and global stability of both disease-free and endemic
equilibrium points when R0 ≤ 1 and R0 > 1 respectively. The most sensitive parameters
for the dynamics of cassava mosaic disease were found to be whitefly mortality rate (ω) and
the whitefly carrying capacity per m2
(κw). The findings from numerical simulation and cost effectiveness analysis on the optimality system conclude that the combined method of roguing
and insecticide application has higher impacts with a lower cost of controlling the disease com pared to the single control approach of roguing activities or insecticide application. Therefore,
for effective and efficient mitigation of cassava mosaic disease, results from this study suggest
the integrated approach of roguing and insecticides applicatio
Combination of natural betanidin dye with synthetic organic sensitiser towards dye-sensitised solar cell application
Get PDFA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Materials Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyBetanidins belong to natural red-purple pigments betacyanins, which experimentally
demonstrated good light adsorption in a visible range and might be suitable for the dye sensitised solar cell (DSSCs). Instability is a well-known drawback of natural dyes, which
impedes their use for DSSCs. A thermodynamic approach helps to understand the betanidin
(Bd) instability which occurs due to spontaneous decarboxylation reaction with
decarboxylated betanidin (dBd) formation. The study considers the improvement of the
sensitiser’s functionality via combination of natural Bd/dBd dyes and synthetic 4-
(Diphenylamino)phenylcyanoacrylic acid (L0) dye. Novel complex D–π–A organic dyes,
L0–Bd and L0–dBd with structural isomers, have been designed via esterification reactions.
The DFT/B3LYP5/6‒31G(d,p) approach has been used to compute geometry, vibrational
spectra and thermodynamic characteristics of the individual isomers and their complexes with
L0. Implementation of TD–DFT method aids in obtaining optoelectronic properties. The
broader coverage of the solar spectrum with greater light-harvesting efficiency was achieved
for the complexes compared to individual dyes. The dyes attachment to the semiconductor
TiO2 was simulated in terms of different adsorption modes to hydrogenated (TiO2)6 cluster.
Binding energies and electronic spectra of the dye@TiO2 systems were computed, and
electron density distributions over frontier molecular orbitals analysed. Binding energy
magnitudes varied within 15‒21 eV for the dye@TiO2 systems
Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers
Get PDFA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyWith the advent of machine learning (ML) techniques, various algorithms have been applied in
previous studies to develop models for predicting soil fertility status. However, these models are
observed to use varying fertility target classes, and variations have been reported in these models'
predictive performances. As a result, practical applications of these models for obtaining the most
accurate predictions may become hindered. While the weighted voting ensemble (WVE) ML
technique can be used to improve soil fertility status prediction by aggregating individual models
prediction, guaranteeing finding of an optimal WVE assignment weights is challenging. Whereas
a brute exhaustive search procedure can be applied for the mentioned task, there is a lack of
exploration on the exploitation of automated classifiers' precise weights combinations as search
spaces for successful optimization. This research aims to develop a high-performance soil
fertility status prediction voting ensemble using brute exhaustive optimization in automated
1EXP(-)Z+ multi-precision weights of hybrid classifiers. Soil chemical properties and ML
modeling algorithms for modeling soil fertility status were identified. Base hybrid ML
classification models for predicting soil fertility status were evaluated using Tanzania as a case
study. Finally, the base ML hybrids WVE models were optimized using brute exhaustive search
procedure’s novel developed search spaces generation algorithm for guaranteed optimal solution
finding. The research was designed using design science research methodology, with the
application of unsupervised machine learning K-mean algorithm with a knee detection method
to find the optimal number of soil fertility status target classes, and supervised learning
algorithms were applied to model classifiers for those optimal classes. Three soil fertility target
classes were identified by clustering technique. The model achieved on test data a predictive
accuracy of 98.93%, with respective AUC of 82%, 83%, and 87% for low, medium, and high
soil fertility targets classes. Whereas these performances are observed higher compared to models
in previous studies, 92% correct classifications were obtained on validation against external
unseen laboratory-based tested soil results. Therefore, soil testing laboratories and farmers should
consider using the model to smartly manage soil fertility which may lead to improved crop
growth and productivity. The government could set agricultural-related policies that require the
use of the model by farmers with the provision of agricultural inputs subsidies. Future work could
be to develop an integrated real-time web and mobile application for providing farmers with soil
fertility status information
Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania
Get PDFA Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Water Resources Engineering of the Nelson Mandela African Institution of Science and TechnologyConjunctive use of surface water and groundwater is rapidly growing in many developing
countries as an adaptation strategy to climate variability and change. However, the interactions
between the groundwater and the surface water systems are not adequately understood, especially
among the East African rift valley lakes, where data paucity has limited studies and reporting on
the spatial influence of catchment heterogeneity. In its humble contribution to sustainable water
development, this study aimed to present a platform for understanding the influence of climatic
variation and anthropogenic activities on surface water–groundwater interactions. To be relevant
locally, Lake Babati, a freshwater lake in Northern Tanzania that provides the community with
fish, freshwater, and a habit for hippopotamus, was studied. The study applied hydrological
simulation, grey relational analysis, and stepwise regression analysis to model the hydrological
behaviour of the lake. Further, it used hydrogeochemistry and environmental isotopes to identify
groundwater fluxes and draw the conceptual understanding of surface water – groundwater
interaction and applied topography-based indices to spatially map groundwater potentials within
the catchment. The results showed that Lake Babati level is significantly declining (p-value < 0.01)
at a rate of 25 mm per annum. The lake level decline could not be explained by climatic variability
since the decline occurred when both evaporation and rainfall showed no significant changes either
seasonally or annually. Instead, the consistent decline of the lake level in all seasons could be due
to the expansion of the spillway, which effectively lowered the lake reservoir level and increased
the lake outflow in rainy seasons. The hydro-geochemistry and isotopes data showed that the lake
water and groundwater interact and are in hydraulic connections. Further, using Height Above
Nearest Drainage based and Topography Wetness Index based methods, the study developed two
groundwater potential maps to predict groundwater spatial variability and guide groundwater
prospecting efforts and subsequent development. Given that Lake Babati is in a hydraulic
connection with the groundwater, its consistent decline will likely impact the groundwater system.
Similarly, abstracting groundwater at unsustainable rates could lower the lake levels further.
Therefore, integrated water resources management is required for sustainable water resources
development and management in the catchment. Mandatory and continuous monitoring of the
water resources (groundwater levels, river flows, and lake levels) is recommended to generate
quality in situ data for future studies
Conservation of forest biomass and forest–dependent wildlife population: Uncertainty quantification of the model parameters
Get PDFA research article was submitted to Heliyon volume 9 (2023)The ecosystem is confronted with numerous challenges as a consequence of the escalating human population and its corresponding activities. Among these challenges lies the degradation of forest biomass, which directly contributes to a reduction in forested areas and poses a significant threat to the survival of wildlife species through the intensification of intraspecific competition. In this paper, a non–linear mathematical model to study the conservation of forest and wildlife species that are reliant on forest ecosystem within the framework of human population dynamics and its related activities is developed and analysed. The study assessed the impacts of economic measures in the form of incentives on reducing population pressure on forest resources as well as the potential benefits of technological efforts to accelerate the rate of reforestation. Qualitative and quantitative analyses reveals that economic and technological factors have the potential to contribute to resource conservation efforts. However, these efforts can only be used to a limited extent, and contrary to that, the system will be destabilised. Sensitivity analysis
identified the parameters pertaining to human population, human activities, economic measures, and technological efforts as the most influential factors in the mode