Search Results (4)

This study deals with the physical, chemical, and thermal properties of William banana peduncle fibers in order to consider the possibility of using these new fibers in textile applications. The samples were collected in Cameroon, in the Littoral region, Njombe Penja district (agri-food industry). The fibers were extracted by three methods, including Water Retting (WR), Dew Retting (DR), and Mechanical Extraction (ME). The various resulting fibers were characterized by X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Fourier-Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscopy (SEM), respectively. The FTIR analysis confirmed the lignocellulosic structure of the fibers and revealed that the three extraction methods had not affected the chemical nature of the fibers. The extraction methods also had no significant impact on density and moisture content. Scanning electron microscopy showed bands of fibers bundles on all samples. Thermogravimetric analysis (TGA) showed that the fibers extracted were thermally stable at 82 °C. X-ray diffraction (XRD) analysis showed crystallinity levels ranging from 58.24% for (WR), 54.83% for (DR), and 69.53% for (ME). The results obtained on the chemical composition show that the extracted fibers consist mainly of 71.8%, 73.6%, and 74.8% cellulose for WR, DR, and ME, respectively, making them suitable for textile applications. Full article

Natural resource-endowed landscapes in many parts of the Global South play a crucial role in the livelihoods of communities. Such resource-endowed areas attract current and prospective resource-use actors, making them veritable hollow frontiers. Hollow frontiers, as crucial resource attractions in many parts of sub-Saharan Africa (SSA), have attracted significant interest in scientific and policy circles. While studies have explored the patterns of migration and population change around hollow frontiers, there is limited evidence on the resource-use dynamics and trajectories in hollow frontiers. This study uses the case of the Mungo Corridor of Cameroon, a hollow frontier par excellence, to (1) determine the variations in forestland resource-use practices, and (2) analyze changes in forestland resource space in the corridor. Data for this study was collected through key informant interviews (n = 37), focus group discussions (n = 15), household surveys using a structured questionnaire (n = 250), and Landsat images. Geospatial analysis, descriptive statistics, and the chi-square statistical technique were employed in the analysis. The study revealed that forestland resource-use practices (NTFPs harvesting) witnessed a significant decline due to the intensification of extraction rates. Furthermore, forestland witnessed a significant decline in Njombe-Penja and Loum (35.216% and 48.176%, respectively) between 1984 and 2024. The results provide novel insights on the pattern of resource use around hollow frontiers and further informs land management policy in the context of the regulation of land-based resources in the hollow frontiers of Cameroon and similar sub-Saharan African contexts. Future studies should explore forestland resource regeneration strategies in the Mungo Corridor. Full article

Innovation diffusion has been extensively explored in several contexts, with little application to hollow frontiers—economically attractive areas with resource opportunities. This dearth of scientific data bedevils the design of resource-planning approaches for hollow frontiers. Using the innovation diffusion model as an analytical lens, this study; (1) maps key resources and actors engaged in the Mungo corridor hollow frontier, (2) analyzes the differential patterns of innovation and the forces that drive them, and (3) explores the management implications of resource-linked innovation diffusion. A random sample of 100 households was conducted in the Njombe-Penja communities of the Mungo Corridor. The analysis reveals the following: Firstly, rich fertile soils, forests, quarries and water resources are the key resources exploited by farmers, bureaucrats, businessmen, NGOs and other enterprises who constitute the key innovation actors in Njombe-Penja. Secondly, differential patterns of innovation exist, with techniques and products introduced by companies/state departments to farmers and by NGOs and technical/research departments to communities and state, private and community-based enterprises. This leads to varied resource use outcomes. Innovations are significant in the domain of agriculture (62%) and quarrying (65%), moderately significant in the forest sector (55%) and least significant in water resources (48%). Economic (63%), natural (25%), socio-cultural (23.7%) and political drivers (50%) explain the diverse outcomes linked to innovation diffusion. Finally, the most significant implications of innovations are in the domain of agriculture through improved cropping systems and the use of disease-resistant species. Here, specialization in cash crop cultivation and fruit culture (white pepper, pawpaw, pineapples) is predominant. Additionally, this sector has witnessed the introduction of new methods of cultivation. The least significant implications were recorded for forest use practices. Innovations sustain new dynamics for this resource-rich area and provide new opportunities for reflections on reorganizing resource use for successful management. Power manifestations in the context of the diffusion of innovations are new areas to consider for further research. Full article

The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874. Full article