This study sought to understand the health risks of foodborne pathogens in fresh leafy vegetables that are grown and consumed locally in Yaounde, Cameroon. Through a survey, 200 respondents were recruited to relate possible food-related illnesses to leafy vegetable consumption. Additionally, a total of 168 vegetable samples consisting of six leafy vegetables and 15 irrigated water samples from five water sources were collected from farms and local markets for microbiological analysis. Using a high-fidelity DNA polymerase, five potential bacterial pathogens, namely, Shiga-toxin producing Escherichia coli (STEC), Campylobacter spp., Salmonella spp., Listeria monocytogenes and Yersinia enterocolitica were also examined. The mean counts of total viable count and total coliforms followed decreasing trends from vegetables obtained on the farms to the local markets, and these ranged from 4.98-8.74 log cfu/g and 1.77-7.42 log cfu/g respectively. All pathogens detected were of significant concern to public health showing high occurrence in some vegetables: STEC (20%) and Yersinia enterolitica (13%) in cabbage, Campylobacter spp. (21%) in lettuce, Listeria monocytogenes (15%) in African nightshade, and Salmonella spp. (15%) in amaranth. Importantly, 42% of respondents highlighted that they frequently got sick from eating leafy vegetables from the study area. These microbiological and qualitative results along with certain vegetable farming and vending practices (such as the use of untreated sewage water for crop irrigation, the sales of physically dirty, muddy, and unpackaged vegetables) indicated that foodborne diseases could be occurring among leafy vegetable-consuming populations in Cameroon.
Mining practices in Cameroon began since the colonial period. The artisanal mining sector before independence contributed to 11–20 % of GDP. From 2000, the rich potential of the Cameroonian subsoil attract many foreign investors with over 600 research and mining permits already granted during the last decade. But, Cameroonian forests also have a long history from the colonial period to the pre-sent. However, mining activities in forest environments are governed by two different legal frameworks, including mining code i.e. Law No. 001 of 16 April 2001 organizing the mining industry and Law No. 94-01 of 20 January 1994 governing forests, wildlife and fisheries. Therefore, in the absence of detailed studies of these laws, there are conflicts of interests, rights and obligations that overlap, requiring research needs and taking appropriate decisions. The objective of this research in the Lom and Djérem division is to study, apart from the proliferation of mining li-censes and actors, the dilemma as well as the impact of the extension of mining activities on the degradation of forest cover. Using geospatial tools through multi-temporal and multisensor satellite images (Landsat from 1976 to 2015, IKONOS, GEOEYE, Google Earth) coupled with field investigations; we mapped the dynamic of different forms of land use (mining permits, FMU and protected areas of permanent forest estate) and highlighted paradoxically the conflict of land use. We came to the conclusion that the rhythm of issuing mining permits and authorizations in this forestall zone is so fast that one can wonder whether we still find a patch of forest within 50 years.
This chapter proposes a remote sensing multi-angles methodology to assess the transition at the interface of the forest-savanna land cover. On Sentinel2-A median images of successive dry seasons, three referential and nine analytical spectral indices were computed. The change vector analysis (CVA) was performed, selecting further one magnitude per index. The averaged moving standard deviation index (aMSDI) was proposed to compare spatial intensity of anomalies among selected CVA, and then statistically assessed through spatial and no-spatial autoregression tests. The cross-correlation and simple linear combination (SCL) computations spotted the overall anomaly extent. Three machine learning algorithms, i.e., classification and regression trees (CART), random forest (RF), and support vector machine (SVM), helped mapping the distribution of each specie. As result, the CVA confirmed each index ability to add new information. The aMSDI gave the harmonized interval [0–0.083] among CVA, confirmed with all p−values=0, z−scores>2.5, clustering of anomaly pixel,and adjusted R2≤0.19. Three trends of vegetation distribution were distinguished with 88.7% overall accuracy and 0.86 kappa coefficient. Finally, extremely affected areas were spotted in upper latitudes towards Sahel and desert.
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