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Access status: Open Access ,
Effects of basal fertilizers type and variery on yield and quality of flue cured tobacco (Nicotiana tabacum.)
(BUSE, 2026-06) Mavhondo, Munyaradzi
The cost of producing flue-cured Virginia tobacco in Zimbabwe has risen sharply, with basal fertilizer accounting for a significant proportion of total production expenditure. The study evaluated the effects of basal fertilizer type and variety on the growth, yield and quality of flue-cured Virginia tobacco (Nicotiana tabacum L.) under rain-fed conditions in Dotito, Zimbabwe. The experiment was conducted using a factorial Randomized Complete Block Design (RCBD) with three replications. Two tobacco varieties (KRK26 and KRK76) and three fertilizer treatments namely Compound B (10:24:20), Compound C (5:15:12) and an unfertilized control were evaluated. Data collected included plant height, leaf area, number of leaves remaining after harvesting stages, dry leaf mass, cured leaf quality and yield. Analysis of variance (ANOVA) was performed using GenStat Version 18 and treatment means were separated using the Least Significant Difference (LSD) test at a 5% level of significance. Results showed that there were no significant interactions between fertilizer type and variety for all measured parameters (p>0.05). Variety had no significant effect on growth, quality or yield of tobacco. Fertilizer type significantly influenced plant height, leaf area, number of leaves, dry leaf mass, cured leaf quality and yield. Both Compound B and Compound C significantly outperformed the unfertilized control in all measured variables. Compound C produced the greatest plant height (84.39 cm), while Compound B recorded the highest leaf area (1928 cm²) and highest yield (1.739 kg). However, no significant differences were observed between Compound B and Compound C for any measured parameter. The control treatment consistently produced the lowest growth, quality and yield values. The study concludes that both Compound B and Compound C effectively improved tobacco growth, yield and quality compared to no fertilizer application. Since Compound C achieved comparable performance to Compound B despite its lower cost, it offers a more economically viable basal fertilizer option for smallholder tobacco farmers.
Access status: Open Access ,
Comparative analysis of three different tomato varieties for yield and fruit cluster traits in greenhouse production
(BUSE, 2026-06) Vusani, Cleopas T
Selection of appropriate cultivars is vital for maximizing productivity and returns in greenhouse tomato (Solanum lycopersicum L.) production. This study evaluated cluster traits (fruit number per cluster, cluster weight and fruit size) and yield parameters (total and marketable yield per plant) of three commercial varieties namely Candela F1, Dylla F1 and Trinity F1. The trial was conducted under greenhouse conditions in Zvishavane, Midlands Province, Zimbabwe. A Randomized Complete Block Design (RCBD) with six replications was utilized with three treatments. The Analysis of Variance (ANOVA) was executed via Genstat 22nd Edition with means separated using Least Significance Difference (LSD) test (p<0.05). The results indicated significant phenotypic variations across all evaluated traits leading to the rejection of all null hypotheses. Dylla F1 produced the highest fruit count per cluster (8.83) followed by Candela F1 (7.67) and Trinity F1 (6.83) with F probability of 0.028. However, Candela F1 yielded the heaviest cluster weight of 1520g and the largest individual fruits size of 75.67mm which significantly outperformed Dylla F1 (1106g and 63.67mm) and Trinity F1 (1024g and 66.33mm) with F probability less than 0.001. Candela F1 achieved the highest total yield of 17.42kg/plant and marketable yield of 14.11kg/plant in terms of final productivity. The study concluded that Candela F1 is the optimal variety for this semi-arid region, successfully combining superior fruit size, cluster weight and commercial yield. While Dylla F1 indicated high fruit set capabilities, it suffered from a physiological trade-off where high fruit numbers compromised individual fruit sizing and marketability. Greenhouse growers are recommended to adopt Candela F1 for commercial production especially when targeting premium markets.
Access status: Open Access ,
Effects of Arbuscular Mycorrhizal fungal and Rhizobia on plant growth of cowpeas CB2
(BUSE, 2026-06) Tigere, Tanaka
Cowpea (Vigna unguiculata) is an important grain legume widely grown in sub Saharan Africa for it’s nutritional and economic value. However, low soil fertility particularly nitrogen and phosphorus deficiency limits the productivity of the crop. This study evaluated the effects of Arbuscular Mycorrhizal Fungal and Rhizobia on the growth and development of cowpea. The experiment was conducted using eight treatments, growth parameters included plant height, root length and mass , nodule weight and number and accumulated biomass were assessed at different growth stages. The results indicated that inoculated treatments significantly improved cowpea growth compared to control. The study concludes that co-inoculation of AMF and Rhizobia has a significant potential to enhance cowpea growth and could be recommended as an effective biofertilizerztion strategy for sustainable crop production.
Access status: Open Access ,
Evaluation of early maturing provitamin a maize hybrids for yield and agronomic performance under different environments in Zimbabwe
(BUSE, 2026-06) Sibanda, Sibonginkosi
Maize (Zea mays L) is the major staple food in sub-Saharan Africa, including Zimbabwe but is nutritionally deficient in provitamin A which has led to high levels of vitamin A deficiency (VAD) in Zimbabwe. Provitamin A (PVA) biofortified maize with high carotenoid content was developed to curb vitamin A deficiency (VAD) but adoption has been constrained by perceptions that biofortified hybrids yield less than conventional maize. This research evaluated grain yield and agronomic performance of twenty-one newly developed early maturing CIMMYT provitamin A maize hybrids alongside four checks (ZS242, ZS244, ZS500 and CZH1815A) to identify superior genotypes for further testing and release. Trials were conducted at three research stations in Zimbabwe (Harare, Gwebi and Rattray Arnold). They comprised of seven environments representing optimum management, low nitrogen stress and combined Grey Leaf Spot (GLS) and Exserohilum turcicum (ET) disease inoculated environment. The results showed that genotype, environment and genotype x environment (G X E) interaction effects were highly significant for grain yield and several traits. Under optimum conditions, the top hybrids DSH24135 (7.81 t/ha), DSH24137 (7.79 t/ha) and DSH24139 (7.45 t/ha) were above commercial check, ZS500 (7.28 t/ha). Low-nitrogen stress reduced mean grain yield by approximately 57%, with DSH24147 (4.29 t/ha) and DSH24132 (3.79 t/ha) showing the greatest nitrogen-stress tolerance. Under disease pressure DSH24142 (4.96 t/ha) and DSH24137 (4.86 t/ha) combined high yield with low disease severity. GGE biplot analysis identified DSH24133 and DSH24139 as the most stable, high-yielding genotypes across two distinct mega-environments. DSH24135, DSH24137 and DSH24139 are recommended for advancement to further multi-location trials
Access status: Open Access ,
Smart network monitoring system using AI-powered anomaly detection and performance optimization
(BUSE, 2026-06) Zifungo, Jacob A.
The aim of this project was to design and develop a Smart Network Monitoring System using AI-powered anomaly detection and performance optimisation to improve the efficiency, reliability, and security of computer networks. The project addressed the limitations of traditional network monitoring approaches, which are largely reactive, manual, and unable to proactively detect anomalies, leading to delayed fault detection, network downtime, and inefficient management. The system was developed using a mixed-methods design science approach involving requirements analysis, system design, implementation, and experimental evaluation. Network telemetry was collected through SNMP, Syslog, and NetFlow protocols and processed using an Isolation Forest machine learning model implemented in Python with scikit-learn, with results stored in MySQL and visualised through a PHP and Chart.js web dashboard. Evaluation against six simulated anomaly scenarios demonstrated a 100% detection rate, an overall F1 score of 0.93, a reduction in false positive rate from 38% to 2.9%, and an average mean time to detect of 25 seconds compared to over four minutes for the threshold-based baseline. The system successfully demonstrated that AI-powered monitoring is practically deployable in resource-constrained organisational environments and delivers measurable, operationally significant improvements over conventional monitoring approaches.