The National Research Repository of Uganda - NRU

Welcome to the National Research Repository of Uganda, abbreviated as "NRU". NRU was established in 2021. NRU is a collection of scholarly output by researchers from the UNCST Community, including scholarly articles and books, electronic theses and dissertations, conference proceedings, journals, technical reports and digitised library collections. It is the official Institutional Archive (IA) of UNCST.

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For information about the publishers' copyright policy on archiving your articles online or in an institutional archive, visit the Sherpa Site at http://www.sherpa.ac.uk/romeo.php The site gives a summary of the permissions normally given as part of each publisher's copyright transfer agreement. If you wish to publish your research findings in the NRU, please contact NRU administrator at admin@uncst.go.ug for details. NRU operates both open access and closed access models. Access to fulltext has been restricted in adherence to the UNCST Intellectual Property Rights (IPR) and Copyrights policies.

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Africa Portal is an online repository of open access library collection with over 3,000 books, journals, and digital documents on African policy issues. This is an initiative by the Centre for International Governance Innovation (CIGI), Makerere University (MAK), and the South African Institute of International Affairs (SAIIA). Please visit the Africa Portal at http://www.africaportal.org/library.

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Now showing 1 - 5 of 12

Recent Submissions

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Soybean Rust Resistance and Yield Performance of Elite Soybean Genotypes Across Diverse Environments in Uganda
(wiley, 2026-02) Tonny Obua;; Julius Pyton Sserumaga;; Godfree Chigeza ;; Alex Malaala;; Mercy Namara;; Bruno Awio;; Solomon Okello;; Phinehas Tukamuhabwa
ABSTRACT Soybean (Glycine max (L.) Merr.) is one of the important legumes globally, serving as an affordable and valuable protein source for humans and livestock. However, selecting the most suitable genotype across diverse environmental conditions remains a major challenge due to significant genotype‐by‐environment interactions (GEI). In addition, the quantitative inheritance of resistance to soybean rust and grain yield further complicates breeding efforts. This study aimed to assess the performance and stability of newly developed soybean genotypes regarding resistance to soybean rust and grain yield. Twenty‐two newly developed genotypes and two check varieties were evaluated using a randomized complete block design (RCBD) with three replications during five consecutive cropping seasons in six distinct locations in Uganda. GEI patterns were examined, and stable, high‐performing genotypes were found using genotype and genotype‐by‐environment (GGE) biplot analysis. The effects of genotype, environment, and genotype‐by‐environment interactions (GEI) on soybean rust resistance, hundred‐seed weight (HSW), and grain yield were all highly significant (p ≤ 0.01). The study revealed that genotypes 6N × SG‐P‐3‐2, 6N × SG‐P‐2, and 6N × SQ‐7 consistently performed better than all the other genotypes for soybean rust resistance, hundred seed weight, and grain yield across the six locations and five cropping seasons. Notably, these genotypes also demonstrated high stability for the three critical traits, making them strong candidates for varietal release. The results of this study provide valuable and new insights for soybean breeding programs in Uganda and the broader Sub‐Saharan Africa, offering a pathway for the development and release of rust‐resistant, high‐yielding soybean varieties adapted to varying agro‐ecological zones.
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AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda
(MDPI AG, 2026-01) Amanya, Geofrey;; Hashmi, Sumbul;; Stow, Jessica Sarah ;; Tumwesigye, Philip;; Nkhata, Bernadette;; Mubiru, Kelvin Roland;; Budts, Anne-Laure;; Potgieter, Matthys Gerhardus;; Balcha, Seyoum Dejene;; Bamuloba, Muzamiru;; Zitho, Andiswa;; Henry, Luzze;; Nabukenya-Mudiope,; Mary G.;; Van Cauwelaert, Caroline
Tuberculosis remains a major public health concern in Uganda, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-control platform, an AI-driven predictive modelling tool, to predict community-level hotspots and support data-driven active case-finding (ACF). Using retrospective chest X-ray screening data, we integrated demographic, environmental, and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the model identified significantly higher TB yields in hotspot areas (risk ratio = 1.69, 95% CI 1.41–2.02; p < 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact. Publicly Available Content Database
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Prediction of maize yield in Uganda using CNN-LSTM architecture on a multimodal climate and remote sensing dataset
(Springer, 2026-01) Danison Taremwa;; Emmanuel Ahishakiye;; Aggrey Obbo ;; Paul Kategaya Kisozi;; Fred Kaggwa
Maize is a staple crop in Uganda, underpinning both food security and rural livelihoods. Accurate forecasting of maize yields is therefore crucial for guiding agricultural planning, resource allocation, and policy design. Yet traditional statistical methods are often limited by low accuracy, poor scalability, and weak integration of diverse inputs, leaving them unable to capture complex, nonlinear, and spatiotemporal dynamics of crop growth. To overcome these constraints, we developed a hybrid convolutional neural network and long short-term memory (CNN-LSTM) model. This model integrates remotely sensed climatic variables and vegetation indices with biannual maize yield records from Uganda’s Zonal Agricultural Research and Development Institute (ZARDI) zones for the period 2018–2020. Due to the scarcity of high-quality yield data, we applied the Synthetic Minority Oversampling Technique for Regression (SMOGN) alongside feature selection to balance the dataset and improve predictive robustness. The CNN-LSTM model’s ability to select features and perform extensive hyperparameter tuning enabled it to outperform baseline models. It achieved a Mean Squared Error (MSE) of 0.107 tonnes2 , a Mean Absolute Error (MAE) of 0.267 tonnes, a Root Mean Squared Error (RMSE) of 0.327 tonnes, and an R2 score of 0.783. A comparative analysis revealed that the CNN+Random Forest (RF) model achieved an MSE of 0.137 tonnes2 , a MAE of 0.281 tonnes, an RMSE of 0.370 tonnes, and an R2 score of 0.722. These results outperformed the standalone CNN (MSE=0.216, R2=0.562) and RF (MSE=0.211, R2=0.573) models, underscoring the advantage of combining spatial–temporal learning for improved predictive accuracy. Residual analysis further confirmed the model's stability, showing minimal bias and close agreement between observed and predicted yields. These findings highlight the potential for integrating spatial– temporal deep learning and ensemble methods to deliver accurate crop yield forecasts in data-limited smallholder systems. By offering a scalable framework for evidence-based farm planning and food security policy, our study demonstrated that advanced machine learning can directly support sustainable development in subSaharan Africa. Future research will extend the framework to incorporate Transformer architectures, high-resolution satellite imagery, and explainable AI, further enhancing accuracy, interpretability, and decision-support capacity. Article highlights • Developed a hybrid CNN-LSTM model that integrates remotely sensed climatic and vegetation indices to predict maize yields across Uganda’s ZARDI zones (2018–2020). • Achieved high predictive accuracy (MSE=0.107, explaining 78% of yield variation), outperforming standalone CNN, ensemble models such as RF, and CNN-RF. • Introduced SMOGN-based data augmentation and feature selection techniques to overcome data sparsity, a novel approach for yield forecasting in smallholder, data-limited systems. • Demonstrated that hybrid DL frameworks can inform scalable, data-driven agricultural planning, with potential to guide policymakers and strengthen food security strategies in SSA. • Future work will focus on integrating Transformer architectures for improved sequence modelling alongside high-resolution imagery and explainable AI to enhance accuracy, interpretability, and practical decision support.
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Draw inspiration from research on the insecticidal, irritant and mosquito-repellent properties of plants used by chimpanzees to build their nests
(BMC, 2026-01) Eurydice Peti-Jean;; Camille Lacroux;; Harold Rugonge ;; Xavier Fernandez;; Djallel Mansouri;; Fabrice Chandre;; Marie Rossignol;; Sophie Durand;; Kevin Calabro;; Sabrina Krief
Abstract Background Vector-borne diseases are still responsible for the deaths of one million people worldwide every year, particularly in African countries. Plans to combat this worldwide burden, including strategies to control vectors, are still being investigated. Among them, the behavior of chimpanzees, our closest relatives living in African forests, has been studied. In Kibale National Park in Uganda, chimpanzees ingest plants that are biologically active against Plasmodium falciparum responsible for malaria but also select tree species to build their nests. The essential oils extracted from their leaves have repellent effects on Anopheles gambiae, which are vectors of Plasmodium falciparum. Methods To investigate the chemodiversity of trees used by chimpanzees, essential oils (EOs) from the leaves of Vepris nobilis, Lepisanthes senegalensis, Turraeanthus africanus, and volatile extracts from the leaves of Celtis africana, which are not used for nesting by chimpanzees, were studied via gas chromatography‒mass spectrometry. The repellent, irritant and toxic activities of the compounds selected on the basis of their abundance, availability and previously studied properties were subsequently tested under laboratory conditions alone and in mixtures on female An. gambiae. Results Volatile compounds abundant at concentrations greater than 0.1% in the four plants were identified. We demonstrate different chemical profiles between the three EOs and the volatile extract, with molecules present in the essential oils such as β-elemene, δ-elemene, caryophyllene, α-humulene, or germacrene D. Chemical families specific to Celtis africana include aldehydes, ketones, carboxylic acids, furans, and vinylphenols. Only linalool was present in all four extracts. The mix we prepared and tested on mosquitoes, which contained α-humulene, caryophyllene, linalool and citral, is toxic and irritant to An. gambiae. Conclusions This study describes volatile compounds present at more than 0.1% in the leaves of four species of Ugandan trees. Certain molecules present only in species used by chimpanzees in their nests can be combined to prepare solutions with anti-mosquito properties. The outcome of this work could lead to the formulation of a repellent spray inspired by chimpanzee behavior and the environment against An. gambiae to add a means of malaria prevention
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Multilevel analysis of factors associated with abortion among adolescents in Uganda insights from UDHS 2022 dataset
(Public Library of Science, 2026-01-12) Stephen Mungau;; Joan Nanteza;; Genevieve Dupuis
Unsafe abortion is a major reproductive health challenge, causing 7.9% of global maternal deaths and 9.6% in East Africa. In Uganda, about 8% of maternal deaths result from unsafe abortions. Early sexual activity, poor access to sex education, restrictive laws and stigma push adolescents into unsafe practices. Limited safe services force many to use dangerous methods leading to severe complications and high maternal mortality. This study examined determinants and prevalence of abortion among Ugandan female adolescents using the 2022 Uganda Demographic and Health Survey dataset of 5,125 females aged 15-24 who had ever engaged in sexual activity. The dependent variable was binary (1 for ever terminated, 0 for never). Weighted data were analyzed using descriptive statistics, ordinary and mixed effect logistic regression models to explore individual- and cluster-level influences. Intra-class correlation and likelihood ratio tests assessed cluster variation. Findings showed 562 adolescents had ever aborted. Those whose first sex was before age 15 were 3.44 times more likely to abort compared to those aged 20-24 while those aged 15-17 were 2.24 times more likely. Married adolescents had twice the odds compared to never married, and cohabiting adolescents were 2.44 times more likely. Compared to those with education beyond secondary, adolescents with no education, primary and secondary schooling were 5.8, 2.99 and 3.01 times more likely to abort. Regional variations accounted for 16.8% of variance, with intra-class correlation of 4.9%. Overall, 11.0% of Ugandan female adolescents reported abortion. Key determinants included age at first sex, marital status, education, contraceptive use and internet use. Region-level factors contributed 4.9% of variation highlighting the need for cluster-level interventions alongside individual approaches.