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ItemDeterminants of extension service delivery on greenhouse gas emissions mitigation in the live-stock sector in central and western Uganda(Makerere University, 2026)Despite recognition of greenhouse gases (GHG) as a major contributor to climate change, Uganda continues to experience low uptake of GHG mitigation practices. Agricultural extension service providers are expected to play a pivotal role in disseminating climate-smart technologies, yet little empirical evidence exists on factors shaping their delivery. This study examined the factors influencing extension service delivery on GHG mitigation, with a focus on livestock waste management. Quantitative and qualitative data were collected from extension service providers in selected districts of central and western Uganda. Descriptive statistics summarized respondent characteristics and Likert-scale ratings used for perceived knowledge on mitigation practices, delivery methods and perceptions. A competence index was constructed from these three areas, and a double hurdle regression model was applied to identify key determinants of delivery and intensity. The results show that extension service providers generally hold positive perceptions, but technical knowledge of specific mitigation practices is limited. Competence varied significantly by institutional affiliation, with private staff demonstrating moderate competence and government staff remaining at low to very low levels. Regression analysis revealed that climate-smart agriculture training, experience, and competence significantly influence service delivery. Qualitative results further revealed that resource access and institutional coordination were key drivers. The study concludes that while positive perceptions are important, extension service delivery on GHG mitigation is driven by extension staff’s experience, competence, access to CSA training and resources. Strengthening CSA training, enhancing resource facilitation and institutional collaboration are recommended to enhance the reach and effectiveness of extension services for GHG mitigation in Uganda’s livestock sector.
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ItemGenetic analysis of pasting properties of selected cassava genotypes in Tanzania for baking applications(Makerere University, 2026)Partial substitution of wheat flour with high-quality cassava flour (HQCF) in baked products can increase cassava utilization, support cassava commercialization and reduce dependence on imported wheat in Tanzania. However, successful cassava-wheat flour substitution requires HQCF with appropriate pasting properties harvested at appropriate times. Information on the pasting properties of Tanzanian cassava genotypes and their appropriate harvesting time for producing HQCF with desirable properties remains limited. Furthermore, rapid development of cassava varieties with targeted pasting properties is constrained by the high cost and time consuming conventional phenotyping methods. Portable near-infrared spectroscopy (NIRS), such as the SCiO™ sensor, offers a potential high throughput rapid alternative tool for screening large breeding populations at early stages in the breeding cycle. However, its application for predicting cassava HQCF pasting properties has not been adequately evaluated. In addition, limited knowledge of genomic regions and candidate genes associated with pasting properties constrains marker-assisted improvement of cassava for specific end uses. This study aimed to: (i) identify suitable varities × harvesting time combinations for desirable pasting properties for baking applications; (ii) evaluate the potential of SCiO™ NIRS for rapid screening of cassava genotypes for pasting properties; and (iii) identify putative genomic regions and putative candidate genes associated with key HQCF pasting properties using genome-wide association studies (GWAS). For the first study, eleven cassava genotypes were evaluated at TARI-Ukiriguru during the 2020/2021 and 2021/2022 seasons and harvested at 10 and 12 months after planting (MAP). For the second study, 221 diverse genotypes were planted at TARI Ukiriguru and Chambezi experimental station during same seasons using an augmented design. For the third study, 236 diverse cassava genotypes were evaluated at TARI-Ukiriguru and Chambezi experimental stations during the same seasons using an augmented design. HQCF pasting properties were determined using a Rapid Visco Analyzer (RVA), while SCiO™ was used to acquire nearinfrared spectra. Genotype and harvest-time effects were analyzed using general linear models in R. For NIRS prediction, Gradient Boosting Machines (gbm), Linear Model (lm), and Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) were used for model calibration and validation. GWAS and putative candidate gene annotation was conducted using appropriate statistical packages in R. Kipusa recorded the lowest setback viscosity both at 10 and 12 MAP, indicating its potential for baked products requiring a softer texture and reduced firming after cooling. In contrast, Hombolo recorded the highest setback viscosity, indicating its potential for products requiring greater structural stability after cooling. For SCiO™ prediction, glmnet with first-derivative spectral pretreatment produced the best models for peak viscosity and breakdown, with validation coefficients of determination (R²) of 0.51 and 0.66, respectively. The corresponding calibration R² values were 0.56 and 0.66. Prediction of pasting temperature was best achieved using a combination of spectral pretreatments. These results demonstrate the potential of SCiO™ NIRS for screening cassava genotypes for selected pasting properties. GWAS identified 48 putative significant SNPs associated with the six evaluated pasting properties traits and 22 putative candidate genes within the associated genomic regions. These putative genomic regions and putative candidate genes provide potential targets for further validation and development of molecular markers to support breeding for cassava varieties with desirable pasting properties. Overall, the study provides complementary phenotypic, technological, and genetic approaches for improving cassava HQCF for specific end uses. Identification of suitable genotype × harvesttime combinations, demonstration of the potential of SCiO™ NIRS for rapid phenotyping, and identification of these putative genomic regions associated with pasting properties can contribute to more efficient cassava breeding and increased utilization of cassava in wheat-cassava baked products, thereby supporting cassava commercialization and reducing dependence on imported wheat in Tanzania.
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ItemHigh throughput phenotyping and genomics assisted breeding for low hydrogen cyanide in cassava(Makerere University, 2026)Cassava has diverse uses for food and feed. However, the utility of the crop is restricted by the presence of hydrogen cyanide (HCN) in the roots. The HCN not only makes roots bitter to the taste, but can also be poisonous to fatal levels. Limiting societal exposure to high HCN cassava by breeding for low HCN varieties is the most sustainable way of limiting HCN dietary toxicity. However, progress has been slow, partly due to the difficulty in HCN phenotyping. This study aimed to contribute to accelerated breeding of low HCN cassava varieties by developing faster, accurate and reproducible HCN prediction methods to inform selection decisions. Multi-location field trials were established; leaf samples picked three months after planting and genotyped with kompetitive allele specific polymerase chain reaction (KASP) markers and also genotyped by sequencing (GBS). Roots were harvested 12 months after planting for HCN quantification and collection of near infrared spectroscopy (NIRS) spectra. Highly significant differences were observed among clones (p < 0.001) and locations (p < 0.001) with significant clone by environment interactions ranging from (p < 0.05) to (p < 0.001). The HCN phenotype range 1 to 9 (< 50 ppm to 800 ppm. Three KASP markers snpME00404, snpME00405 and snpME00406 showed significant co-segregation of genotypes with HCN phenotype, explaining 27%, 17% and 14% of the HCN phenotypic variation respectively. The NIRS prediction with modified partial least squares (MPLS) algorithm had low accuracy (R2p = 0.38). However, binary classification with machine learning algorithms achieved up to 99% accuracy with partial least squares discriminant analysis (PLS-DA), 75% with support vector machine (SVM) and 74% with logistic regression (LR) using the full spectral range (400 – 2500 nm). The wavelengths 961, 1165, 1403–1505, 1913–1981, and 2491 nm were influential in discrimination of low and high HCN accessions. Using these selected wavelengths, LR achieved 100% classification accuracy. The accuracy of genomic prediction models was low for Bayesian Ridge Regression (BRR) (r = 0.18) and Genomic Best Linear Unbiased Prediction (GBLUP) (r = 0.22) but moderate for Bayes B and C (r = 0.48), and Bayes A (r = 0.49). Reproducing Kernel Hibert Spaces (RKHS) and Bayesian Lasso (BL) models had the highest prediction accuracy (r = 0.52). Collectively, these results offer frameworks for low HCN cassava breeding in Uganda. The NIRS and KASP markers can be used in routine phenotyping and selection in early generation trials while the genomic selection models RKHS or BL can be deployed in cyclic population improvement for low HCN.
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ItemData for estimating GMXL models for the paper "Forecasting the market potential for circular economy inputs: adoption rates and aggregate demand for fecal sludge-derived fertilizer in Uganda"(Makerere University, 2026)Conventional linear agriculture ("extract and dispose") degrades agroecosystems, threatening long-term food security. Transitioning to a circular economy (CE) offers a pathway to sustainable food production, yet its adoption in Sub-Saharan Africa (SSA) is hampered by a lack of market feasibility data for waste-to-value initiatives. Fecal sludge-derived fertilizer (FSDF) is one of the initiatives with a potential to support the CE. This study estimated potential uptake rate of FSDF and its market demand. Using a discrete choice experiment, primary data were collected from 351 farmers in Wakiso District, Uganda and analyzed via a Generalized Mixed Logit model and sensitivity analysis. Results reveal that potential FSDF uptake ranges from 34% to 94%, driven primarily by access to extension services and membership in agricultural groups. Under current socioeconomic and institutional conditions, baseline aggregate demand is estimated at 31,500 tons annually, with the potential to reach 61,000 tons if institutional barriers are addressed. The baseline potential uptake rate of 48% provides a viable foundation for transitioning to CE farming in SSA. To maximize market scale, policymakers should expand agricultural extension access and foster farmer group membership, giving industrialists the empirical evidence needed to align production capacity with market demand.
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ItemGenetic diversity and insecticide resistance of fruit flies in selected mango-growing agro-ecological zones of Uganda(Makerere University, 2026)Fruit flies (Tephritidae) represent a major constraint to mango production and export in Uganda, causing direct fruit damage and leading to frequent export rejections. Accurate species identification is essential for effective pest management, yet studies in Uganda have been limited and primarily relied on morphological methods, which are prone to misidentification. This study employed an integrated approach combining morphological and molecular techniques to assess fruit fly diversity and investigate potential insecticide resistance. Biological surveys were conducted on 20 mango farms across seven districts in Uganda’s two main mango-growing agroecological zones. A total of 256,107 fruit flies were trapped using different lures targeting Bactrocera, Ceratitis, Dacus, Zeugodacus, and other fruit fly species. Morphological identification of the fruit fly species was conducted at Makerere University’s Crop Science Laboratory, followed by PCR confirmation at COVAB’s Research Centre for Tropical Diseases and Vector Control (RTC Lab), and Sanger sequencing at Inqaba Biotec in South Africa. In addition, this study examined the presence of target-site mutations in the ace gene region (214ace), which may confer resistance to organophosphate insecticides, the very widely used class in Uganda. Eleven taxa were identified to the genus level, including Ceratitis, Carpomya, Dacus, Rhagoletis, and Zeugodacus. A total of 21 putative species were documented. Ten were confirmed to species level i.e., Bactrocera dorsalis, Ceratitis capitata, C. cosyra, C. anonae, Dacus punctatifrons, D. bivittatus, D. eclipsis, D. humeralis, D. armatus, and Zeugodacus cucurbitae. Notably, D. eclipsis, D. humeralis, and D. armatus were recorded in Uganda for the first time. Bactrocera dorsalis was the most abundant (95.1%) and widely distributed species, followed by D. eclipsis and D. punctatifrons. These findings reveal a high diversity of fruit fly species in Ugandan mango orchards, underscoring the need for continued monitoring and integrated pest management strategies. No missense mutations linked to resistance were detected in the analysed species, suggesting that the observed species diversity and abundance are not related to mutations in this gene region. However, in D. armatus, a substitution was observed at the 148th position of ORF1, with thymine (T) replacing adenine (A), resulting in a TTG codon (leucine) instead of ATG (methionine). This difference may reflect species-specific genetic variation compared to other Dacus and Zeugodacus species. Absence of mutations linked to resistance means that farmers can continue using organophosphates in an integrated pest management system.