Ounongo Repository
The Ounongo Repository (OR) is the institutional repository of Namibia University of Science and Technology. Ounongo means "knowledge. in the Oshiwambo and Otjiherero languages. The OR is administered by the Library, with technical assistance from DICT, and its aim is to collect, organize, manage, store, preserve, publish and make accessible worldwide, the knowledge assets or intellectual output of the University's researchers, staff and post-graduate students. Users may set up RSS feeds to be alerted to new content.
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Recent Submissions
Technical resource use efficiency of small-scale maize farmers in the Etunda irrigation project, Omusati region in Namibia.
(University of Science and Technology, 2023-09) Tshupo Kitso Baitshoki
Namibia has identified agriculture, among other sectors, as a strategic development area to achieve its vision of economic growth through industrialisation. Maize production is a vital contributor to Namibia's socio-economic development, with an average per capita consumption of 44kg per year, higher than other cereals such as pearl millet. This study aims to investigate the technical resource utilisation efficiency of small-scale maize farmers at the Etunda Green Scheme Irrigation project in the Omusati Region of Namibia.
A total of 47 small-scale farmers at the Etunda Irrigation Scheme were surveyed using a census sampling method and a structured questionnaire to capture data. A Cobb-Douglas function for production under the Stochastic Frontier Model was used to estimate the technical efficiency of producers, whereas an enterprise budget was used to evaluate their profitability. The average technical efficiency of this study is 90%, which means the farmers are using the available resources efficiently. The study reveals that producers’ technical efficiency is influenced by tractor power, seeds and fertilisers. Tractor power, seeds and fertiliser elasticities are 0.761, 0.087 and 0.442, respectively. Farm specific socio-economic factors were modelled to estimate farmer’s technical inefficiency. The results reveal that gender and extension services had a significant and negative impact on the technical efficiency of producers. The results also show that the majority of producers (81%) operated at a loss, with fertiliser accounting for 51% of total production costs.
The study recommends that policymakers incentivise local fertiliser production through availing of funds to citizens and non-citizens who wish to do fertiliser production business or remove taxes on imported fertilisers which will significantly drop production costs. There is a need to provide farmers with comprehensive technical training and robust extension services, ensuring they utilise technical resources to their fullest potential, thereby maximising profitability. It is highly recommended that farmers have intensified access to technical training and effective extension services to enhance their efficient use of technical resources to enhance profitability.
Evaluating the performance of marketing channels for small and medium scale producers of selected vegetables produced using Olushandja-Ogongo canal water, Namibia.
(Namibia University of Science and Technology, 2023-02) Maija S.N. Shetunyenga
Namibia has achieved a significant improvement in local supply of fruits and vegetable throughout the country from both commercial and communal areas. However, vegetable marketing is still a challenge to farmers particularly the small to medium sized. This study evaluated the performance of marketing channels used by small-medium scale vegetable producers utilising Olushandja - ogongo canal water. Using a random sampling procedure, a sample of 53 vegetable producers from the study area was drawn. A survey consisting of a structured questionnaire was used to conduct face-to-face interviews with the vegetable producers. The study focused on the 5 dominant crops grown in the study area, which were; Tomato, Cabbage, Butternut, Onion and Green pepper. Descriptive statistics, logistic regression and marketing efficiency measures were used to analyse data. All vegetable producers under study were involved in informal market channels although there was a reasonable number of producers who also used formal markets, in addition to informal markets. Transport, storage facilities, membership to a marketing association and access to marketing information all had a significant positive relationship with choosing mixed marketing channels when informal markets served as a baseline group through logistic regression. There was a strong correlation between channel length, producer’s share and marketing margin. The study tested for relationships between variables in case of Tomato and butternut which where the most dominant crops. In tomato, the study found a significant relationship when marketing efficiency was regressed against channel length, farmer’s price, marketing cost and marketing margin. On the other hand, channel length and marketing costs had a significant relationship with marketing efficiency in Butternut. Transport cost was the highest marketing cost incurred by producers. The study found that the direct channels had high marketing efficiency with over 100% as compared to intermediated channels. An intermediated channel that involved wholesalers had the least marketing efficiency index. Crops that had high efficiency measures in most channels were Cabbage and Green pepper while Tomato and Butternut had the least efficiency measures. The study calls for marketing bodies to help organise farmers in terms of production and linking them to the markets.
Potential distribution of major plant units under climate change scenarios along an aridity gradient in Namibia
(Vegetation Classification and Survey, 2024-06-13) Leena Naftal; Vera De Cauwer; Ben J. Strohbach
Objectives: Climate change is expected to have major impacts on plant species distribution worldwide. These changes can affect plant species in three ways: the timing of seasonal activities (phenology), physiology and distribution. This study aims to predict the effect of shifting climatic conditions on the major vegetation units along an aridity gradient through Namibia. Study area: Namibia’s vegetation is characterised by open woodland in the northeast to low open shrubland in the southern part of the country. These differences are a result of increasing aridity from north to south with a rainfall gradient from 100 mm to 600 mm. Namibia is projected to have an increase in annual mean temperature of 2°C by the end of the 21st century. Methods: A vegetation classification was done for 1,986 relevés using cluster analysis, a Multi-Response Permutation Procedure and indicator species analysis. The current distribution of the vegetation classes was modelled with Random Forest. Future projections for the most important climate variables were used to model the potential distribution of the vegetation units in 2080. This modelling approach used two scenarios of Representative Concentration Pathways (4.5 and 8.5) from two Global Climate Models – the IPSL–CM5A–LR and HAdGEM2–ES. Results: The predicted distribution shows a high expansion potential of Eragrostis rigidior-Peltophorum africanum mesic thornbush savannas, Combretum africanum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas towards the south under both scenarios. Conclusions: The model indicated the ability to classify and predict vegetation units to future climatic conditions. Half of the vegetation units are expected to undergo significant contraction. Overall, RCP8.5 conditions favour the proliferation of certain vegetation types, particularly Combretum collinum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas, potentially displacing other vegetation types.
Taxonomic reference: Klaassen and Kwembeya (2013) for vascular plants, except Kyalangalilwa et al. (2013) for the genera Senegalia and Vachellia s.l. (Fabaceae).
Abbreviations: CDM = Community Distribution Model; CMIP5 = Coupled Model Inter-comparison Project Phase 5; EVI = Enhanced Vegetation Index; GCM = General Circulation Model; IV = Indicator Value; ISA = Indicator Species Analysis; MAP = mean annual precipitation; MAT = mean annual temperature; MRPP = Multi-Response Permutation Procedure; NMS = Non-Metric Multidimensional Scaling; RF = Random Forest; RCPs = Representative Concentration Pathways; SDM = species distribution model.
The assessment of UAV-based multispectral imagery to monitor lichen-dominated biological soil crusts response to fog and disturbances, Central Namib
(Namibia University of Science and Technology, 2022-10) Nambwandja Ailly Nanguloshi
Lichen-dominated biological soil crusts (BSCs or biocrusts) are a major contributor to biodiversity and
ecosystem services of desert environments. There appropriate monitoring of lichen fields of the hyperarid
coastal Namib Desert is important to understanding their greater ecological functions, such as vital
CO2 sinks, and for management of socio-economic land use practices (i.e. mining, tourism, off-road tracks)
which destroy the endemic, fragile and slow recovering lichens. Thanks to advanced remote sensing
techniques such as the use of Unmanned Aerial Vehicles (UAVs or drones), lichen fields can be monitored,
whilst minimising the environmental impact of traditional ground-based ecological research, as well as
overcoming challenges in monitoring lichens with the satellite datasets, piloted aircraft. The objectives of
this study was to assess the effects of hourly sunshine intensity on the use of UAV-based multispectral
imagery for monitoring lichen-dominated biological soil crusts in the hyper-arid Central Namib Desert. We
then assess the applications UAV-based NDVI to observe lichen photosynthetic activity response to
moisture input, topography and disturbances, as well as to assess the influence of micro-topography and
disturbances in off-road tracks on lichen coverage.
We tested the performance of UAV-based Parrot Sequoia multispectral sensor by acquiring time series
flights from 08:00 to 17:00 during a fog day. Time series of radiometrically and atmospherically corrected
and uncorrected multispectral image datasets (i.e. reflectance images and vegetation indices) were
compare it to solar elevation. Influence of brightness effects in sample plot on image values was tested
with one-way ANOVA and Tukey HSD pot-hoc test for pairwise comparisons. There was statistical
significant difference in means of the sample plots with p < 0.05. We also gathered time series UAV-based
NDVI images on fog and non-fog day derived the temporal patterns of lichen photosynthetic activity in
responding to wet and dry conditions, overtime. These UAV observations were supported with ground
cover observation of lichens. Statistical analysis was performed to determine presence of significant
differences of lichen cover and NDVI response between micro-topographic habitats (i.e. ridges, windward
and leeward sides of the relief), and as well in landforms such as plains, river washes and tracks. The oneway
ANOVA and Student-test (t-tests), were considered significant at p < 0.05 for both UAV and ground
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observations. Polynomial regression analysis of the relationship between elevation and ground lichen
cover and NDVI was significantly correlated with p-values of < 0.05.
Well illuminated good quality images can be captured at low solar elevation, in morning images at 08:00
and in the later afternoon at 17:00. Midday images taken between 10:00 and 15:00 at higher solar
elevation are heavily impacted by brightness effects. The radiometrically corrected images and NDVI were
more accurate than the raw uncorrected images. UAV-based NDVI can estimate photosynthetic activity
when lichens are hydrated by fog or higher air humidity, as opposed to when they are desiccated and
photosynthetic activity is inhibited. Elevation is good predictor lichen cover and photosynthetic activity.
Lichen cover is heavily impacted by off-road tracks. The topographic attribute influence the distribution
of lichen cover and lichen species diversity. This research study has provided insights into a means to
monitor these sensitive environments. It also enables informed decision making to manage natural
resource and regulate socio-economic land-use.
Recommending a Machine Learning Model to Detect the Fatigue State for Employees at Namdeb
(Namibia University of Science and Technology, 2024-04) Nakale, S.N.
Workplace fatigue is one of the major risk factors across different industries and it negatively impacts productivity and workplace safety. Thus, fatigue detection and monitoring is essential to promote occupational health and safety. The advancements in data collection technologies have made it possible for industries to develop data driven solutions by developing Artificial Intelligence (AI) and Machine Learning (ML) based fatigue monitoring and detection systems. The Advanced Driver Assistance Systems (ADAS) is one of the technologies that has been adopted in various industries to improve safety for drivers. Namdeb implemented the ADAS in recent years and they have identified the need for a system to detect and classify employees’ fatigue state using data from the ADAS. In this study, an ML based fatigue detection system was proposed. Facial behavioural fatigue features were used to detect fatigue. The proposed system deployed some of the commonly used ML classification algorithms and it was evaluated on a simulated dataset, the Yawning Detection Dataset (YawDD), and a real-world dataset, data from the Namdeb ADAS. The results showed that most of the supervised ML classifiers achieved a fatigue prediction accuracy above 90% for both datasets. The Random Forest (RF) based fatigue detection-based model was found to be the best model. The k-Means which is an unsupervised ML classifier exhibited the worst performance. However, the reliability and generalisability of the results based on the real-world dataset can be improved by using a larger dataset. The major challenge to developing behavioural based fatigue detection systems for real world setting like the mining environment is face detection accuracy which is affected by factors such as low image resolution due to poor and variable lighting conditions, face orientation to camera and proximity of face to the camera. The significant contribution of this study is the use of real-world dataset to test the proposed fatigue detection system. Overall, the study contributes to the promotion of the eighth Sustainable Development Goal (SDG) of promoting safe working environments.