Bioactive constituents and antibacterial screening of two Nigerian plant extracts against selected clinical bacteria

Background: The growing desires to combat antibiotic resistance among pathogenic bacteria necessitate the need to search for new antimicrobials agents from other sources such as plants. Objectives: The present study investigated the antibacterial activities and bioactive components of Nymphaea lotus and Spondias mombin against selected clinical bacteria Material and Methods: Extracts of N. lotus and S. mombin were prepared by 72 hours maceration in 70% methanol. The antimicrobial susceptibility testing (AST) of Escherichia coli, Klebsiella pneumoniae, Enterobacter aerogenes, Salmonella typhi, Staphylococcus aureus, Citrobacter freundi and, Klebsiella oxytoca against the two extracts was carried out by disk diffusion method while minimum inhibitory concentrations (MIC) and minimum bactericidal concentrations (MBC) was by agar-well dilution and broth dilution method, respectively. The bioactive compounds of the plants were identified by Gas Chromatography-Mass Spectrometry (GC-MS) analysis. Results: Extracts of N. lotus showed better antimicrobial activities than S. mombin against all the clinical bacterial isolates with an MIC range of 3.13 – >12.5mg/mL compared to S. mombin with MIC range of 6.25 – >12.5mg/mL. The GC-MS results revealed the presence of 21 and 25 compounds for N. lotus and S. mombin  respectively. Benzoic acid derivatives were in abundance in both plants with  approximately 71.5% and 82.1% in N. lotus and S. mombin respectively.  Conclusions: The findings from this study provided further evidence on their ethno-botanical claims and additional information on the potentials of the studied plants as effective medicinal plants with antimicrobial activity against clinical bacteria. This highlights the need for continuous exploration of medicinal plants for novel  compounds with better antimicrobial property as option for the treatment of resistant bacterial infection Keywords: Nymphaea lotus, Spondias mombin, Bioactive components, Antimicrobial

Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance

Open Access Journal; Published online: 18 Oct 2022Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA

Spatial multivariate cluster analysis for defining target population of environments in west Africa for yam breeding

Yam (Dioscorea spp.) is a major staple crop with high agricultural and cultural significance for over 300 million people in West Africa. Despite its importance, productivity is miserably low. A better understanding of the environmental context in the region is essential to unlock the crop’s potential for food security and wealth creation. The article aims to characterize the production environments into homologous mega-environments, having operational significance for breeding research. Principal component analysis (PCA) was performed separately on environmental data related to climate, soil, topography, and vegetation. Significant PCA layers were used in spatial multivariate cluster analysis. Seven clusters were identified for West Africa; four were country-specific; the rest were region-wide in extent. Clustering results are valuable inputs to optimize yam varietal selection and testing within and across the countries in West Africa. The impact of breeding research on poverty reduction and problems of market accessibility in yam production zones were highlighted

Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in west Africa

Published online: 25 May 2022Soybean (Glycine max (L.) Merr.) is a leguminous and oil crop with rapidly growing importance and demand in Africa following the increasing demand for oil and livestock and poultry feed in sub-Saharan Africa. However, soybean productivity is low in most countries of sub-Saharan Africa, especially in West Africa, where productivity is below one ton per ha. Hence, concerted soybean varietal development and testing efforts have been underway by the International Institute of Tropical Agriculture (IITA), collaborating with the various African and US-based soybean breeding programs. Integrating new varietal evaluation approaches based on advanced phenotyping techniques into IITA's soybean breeding program is crucial for designing efficient crop genetic improvement techniques. Hence, this work aims to investigate machine learning (ML) models and Unmanned Aerial vehicles (UAVs) to aid rapid high throughput phenotypic workflow for soybean yield estimation. We acquired multispectral images through a Sequoia® camera aboard a senseFly eBee X UAV from five variety trials during the 2020 growing season in Nigeria. UAV-based spectral bands, canopy height, vegetation indices (VI), and texture features were generated by gray level co-occurrence matrix (GLCM) and integrated to predict crop grain yield using five machine learning (ML) regression models, including Cubist, Extreme Gradient Boosting (XGBoost), Stochastic Gradient Boosting (GBM), Support vector machine (SVM), and Random Forest (RF). The main findings are the textural information generated using gray level co-occurrence matrix (GLCM) slightly outperformed predictors based mainly on vegetation indices (VI) and provided a promising alternative to the conventional use of VI in crop yield estimation. All the five ML models performed moderately well in predicting grain yield for all the soybean trials investigated, though the Cubist and RF model stood out, with R2 reaching 0.89. The study provides a framework to perform crop breeding trial assessments more effectively and consistently at high spatial scales that African crop breeding programs did not commonly apply. The workflow can also be successfully modified and applied for high throughput phenotyping of breeding platforms in other crops

Comparison of UAV and SAR performance for crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of west Africa

Published online: 22 Aug 2022Food insecurity is one of the major challenges facing African countries; therefore, timely and accurate information on agricultural production is essential to feed the growing population on the continent. A synergistic approach comprising a high-resolution multispectral UAV optical dataset and synthetic aperture radar (SAR) can help understand spectral features of target objects, especially with crop type identification. We conducted this work on the experimental plots using high spatial resolution multispectral UAV data (12 cm, re-sampled to 50 cm) in combination with the Sentinel 1C Synthetic Aperture Radar (SAR) dataset. We generated 11 agronomically relevent vegetation indices from the UAV multispectral image. Multiple combinations of the UAV datasets were analysed to assess the impact of canopy height model (CHM) on classification accuracy and to determine the optimum dataset (including spatial resolution) for the land cover classification. We also appraise the impact of variable spatial resolution on classification accuracy. A combination of VH and VV polarizations of Sentinel-1 SAR data was also analysed to classify the crop types while comparing its accuracy with the UAV-derived models. Our results show that model accuracy is improved- for all the data combination pairs, when CHM is added to the modelling. We also observed a decreasing trend in classification accuracy with respect to increasing spatial resolution. Generally, the support vector machine (SVM) classifier produced an overall accuracy of 94.78% and 81.72% for UAV and SAR datasets, respectively. In comparison, the random forest (RF) achieved an accuracy of 93.84% and 92.58%, for UAV and SAR datasets, respectively. The outputs from ground-based validation corroborate the results from model-based classification coupled with acceptable simple models’ agreement ratio (SMAR), exceeding 90% in some cases. The combined techniques can be useful in precision agriculture over small and large agricultural fields to support food security assessment and planning

Monitoring of land use intensification and linkage to soil erosion in Nigeria and Benin

The growing population and the increasing food demand in sub-Saharan Africa require comprehensive land use intensification. Land scarcity and soil degradation are some consequences that necessitate enhanced land use management by using remote sensing data. This study presents the analyses of aerial photographs and satellite images for inventory land use and its changes within time as well as for monitoring soil erosion. The example from a pilot village located in southern Benin shows the increase of the cropping area and the growth of erosion gullies within the last decades. As the data provide the basis for installing soil conservation techniques, they are a useful tool to restore soil productivity and to cope with the food demand in the country

Geological Carbon Sequestration in the Context of Two-Phase Flow in Porous Media: A Review

This is an Accepted Manuscript of an article published in Critical Reviews in Environmental Science and Technology on 21 May 2014, available online: http://dx.doi.org/10.1080/10643389.2014.924184In this review, various aspects of geological carbon sequestration are discussed in relation to the principles of two-phase flow in porous media. Literature reports on geological sequestration of CO2 show that the aquifer storage capacity, sealing integrity of the caprock and the in situ processes, e.g., the displacement of brine by supercritical CO2 (scCO2), convection-diffusion-dissolution processes involving scCO2 and brine, geochemical reactions, and mineral precipitation depend on the fluid-fluid-rock characteristics as well as the prevailing subsurface conditions. Considering the complexity of the interrelationships among various processes, experimental investigations and network of mathematical functions are required for the ideal choice of geological site with predictable fluid-fluid-rock behaviours that enhance effective monitoring. From a thorough appraisal of the existing publications, recommendations are made for improvement in the existing simulators to fully couple the entire processes involved in the sequestration operations and in situ mechanisms which include injection rate and pressure, brine displacement, simultaneous flow of free and buoyant phases of CO2, various trapping mechanisms, convection-diffusion-dissolution processes, scCO2-brine-rock reactions, precipitation of the rock minerals and the consequences on the hydraulic and hydrogeological properties in the course of time as well as the quantity of injected CO2. Suggestion is made for the inclusion of leakage parameters on site-specific basis to quantify the risks posed by the prevailing fluid-fluid-rock characteristics as well as their immediate and future tendencies. Calls are also made for thorough investigations of factors that cause non-uniqueness of the two-phase flow behaviour with suggestions for the use of appropriate experimental techniques. The review comprehensively synthesizes the available knowledge in the geological carbon sequestration in a logical sequence