Search Results (321)

In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state changes and how temperature affects them. By combining the resulting dataset with satellite-based land-use data, we show the distribution of activities across landscapes and seasons and within a day. We accurately reproduced key aspects of cattle mobility and characterised rarely documented features of Sahel agropastoral practices, such as transhumance phases, nocturnal grazing and in-field rainy season paddocking. These results suggest that our methodology, which we make available, could be valuable in addressing issues related to the future of Sahelian pastoralism. Full article

This article addresses the development of a human-centered shelter design tailored to meet the specific needs of refugees in the Al-Sahel Region. It focuses on five essential aspects of humanitarian-centered design. The goal is to create a livable unit that accommodates the three distinct phases of an emergency, transitional, and durable situation. We have adopted a non-linear design approach to develop the refugee shelter unit. We engage in discussions with team experts following each data collection phase. The conceptual design of the shelter unit is intended to align with the refugee settlement’s natural growth while maintaining a degree of control over its evolution. We have outlined a spatial configuration for a residential unit designed for three to six individuals and various patio options. Additionally, we have devised plans for an education and healthcare facility, all designed with the same structure to bring a more organized approach to the organic growth of the camp. The design proposal adopts a process-oriented approach, incorporating refugees indirectly in the design and construction of their shelters. While we do not assert that the framework of a ‘refugee camp’ can be sustainable, our goal is to show that its planning, in the absence of alternatives, should adhere to sustainability criteria. Full article

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa. Full article

Blue light accelerates retinal aging. Previous studies have indicated that wavelengths between 400 and 455 nm are most harmful to aging retinal pigment epithelia (RPE). This study explored whether filtering these wavelengths can protect cells exposed to broad sunlight. Primary porcine RPE cells loaded with 20 µM A2E were exposed to emulated sunlight filtered through eye media at 1.8 mW/cm² for 18 h. Filters selectively filtering out light over 400–455 nm and a dark-yellow filter were interposed. Cell damage was measured by apoptosis, hydrogen peroxide (H₂O₂) production, and mitochondrial membrane potential (MMP). Sunlight exposure increased apoptosis by 2.7-fold and H₂O₂ by 4.8-fold, and halved MMP compared to darkness. Eye Protect System^TM (EPS) technology, filtering out 25% of wavelengths over 400–455 nm, reduced apoptosis by 44% and H₂O₂ by 29%. The Multilayer Optical Film (MOF), at 80% of light filtered, reduced apoptosis by 91% and H₂O₂ by 69%, and increased MMP by 73%, overpassing the dark-yellow filter. Photoprotection increased almost linearly with blue-violet light filtering (400–455 nm) but not with total blue filtering (400–500 nm). Selective filters filtering out 25% (EPS) to 80% (MOF) of blue-violet light offer substantial protection without affecting perception or non-visual functions, making them promising for preventing light-induced retinal damage with aesthetic acceptance for permanent wear. Full article

In the Sahel region of West Africa, land degradation has raised concerns about the threat of desertification, leading to the establishment of the United Nations Convention to Combat Desertification (UNCCD) in 1994. Over time, the focus has shifted from simply combating desertification to a more comprehensive international program focused on preserving the health of our land by offsetting any damage with restoration efforts by 2030 to sustain ecosystem functions and services. This balancing process—which is in line with the Sustainable Development Goals (SDGs)—is known as Land Degradation Neutrality (LDN). We examine Land Degradation Neutrality (LDN) patterns, namely degradation and rehabilitation processes, by integrating participatory mapping with high-resolution satellite imagery with local stories, observations, historical records, and existing studies. The data elicited an understanding of the processes driving land degradation and adaptation strategies among three distinct ethnic groups of crop and livestock farmers in the village of Yallé in southern Burkina Faso. Some of these people were originally from this region, while others moved from places where the land was already degraded. Participants in the study had diverse experiences and perceptions of land degradation, its drivers, and adaptation strategies, which were influenced by their ethnicity, livelihood activities, and life experiences. These differences highlight the impact of cultural and socioeconomic factors on how people view land degradation, as well as the role of local knowledge in managing the environment. The study emphasizes the necessity of incorporating ethnoecological perspectives into projects focused on Land Degradation Neutrality (LDN) to better understand land degradation and improve land management. This integration can significantly contribute to strengthening global sustainability and community resilience. Full article

The recent commercial availability of stacked dielectric elastomer actuators (SDEAs) has unlocked new opportunities for their application as “artificial skeletal muscles” in rehabilitation robots and powered exoskeletons. Composed of multiple layers of thin, elastic capacitors, these actuators present a lightweight, soft, and acoustically noiseless alternative to traditional DC motor actuators commonly used in rehabilitation robotics, thereby enhancing the natural feel of such systems. Building on our previous research, this study aimed to evaluate the most recent version of commercial SDEAs to assess their potential for mechanizing rehabilitation robots. We quantified the stress and strain behavior and stiffness of these actuators in both single and 1 × 3 configurations (with three SDEAs connected in series). The actuators demonstrated the capability to generate up to 25 N of force and 115 KPa, a value surpassing human biceps, with a longitudinal strain measured at about 11%. The significant increase in force generation from 10 N in the previous version to 25 N and displacement from 3.3% to 11% substantially enhances the applicability of this actuator in rehabilitation robotics. SDEAs’ high force generation capability, combined with their strain and stress characteristics comparable to that of human biological muscles, make them ideal alternative actuators for biomimetic robots and applications where actuators must operate in the vicinity of the human body. Full article

Background: Hypertension is a major risk factor for ischemic stroke. An important strategy in controlling hypertension is dietary modification. The present study evaluates the effect of Dietary Approaches to Stop Hypertension (DASH) diet on the risk of ischemic stroke. Methods: A case–control study was carried out, including 214 ischemic stroke cases recruited within the first 48 h of diagnosis and 214 controls, divided equally into hospitalized and non-hospitalized participants. Controls were matched to cases based on age and gender. Socio-demographic characteristics were assessed, in addition to adherence to the DASH diet, which was measured using a preconstructed DASH diet index (ranging from 0 (lowest) to 11 (highest)). For stroke patients, Modified Rankin Score (mRS) was measured to assess disability. Results: Smoking, hypertension, hyperlipidemia, atrial fibrillation, and myocardial infarction were significantly associated with ischemic stroke (p < 0.001). Higher adherence to the DASH diet was correlated to lower rates of stroke, where cases scored 5.042 ± 1.486 compared to 6.654 ± 1.471 for controls (p < 0.001). Eating more grains, vegetables, fruits, dairy products, nuts, seeds, and beans, and lower levels of fat, fewer sweets, and less sodium were associated with lower rates of ischemic stroke (p = 0.038 for sweets and p < 0.001 for all the remaining), while meat, poultry, and fish did not have any significant effect (p = 0.46). A multivariate analysis showed that lower adherence to the DASH diet (p < 0.001, OR: 0.526, CI95% 0.428–0.645) was associated with a higher incidence of ischemic stroke and an increased likelihood of having high disability levels (mRS 5–6) (p = 0.041, OR: 2.49 × 10⁻⁸, CI95% 0–2.49 × 10⁻⁸). Conclusions: The relation between the DASH diet and risk of stroke highlights the necessity for strict adherence to dietary restrictions, suggesting a protective role for the DASH diet in stroke pathogenesis and prognosis. Full article

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%. Full article

Supplemental irrigation based on runoff harvesting is a sustainable solution in the current context of water scarcity that is prevalent in Sahelian countries. Runoff water harvesting ponds (RWHPs) are increasingly being utilized for vegetable cultivation by producers. This study aims to analyze the cropping choices of producers benefiting from RWHPs in the Kadiogo and Bazèga provinces of Burkina Faso in the West African Sahel. A sample of 27 surveyed producers revealed a dominant preference for vegetable crops (93.46% of the total production) over cereals. The cropping choices are influenced by factors such as the crop resistance to dry spells, the water demand, the economic return, and the market demand. For the effective utilization of the basins, crop choices should consider the water retention capacity of the basin. Additionally, to enhance the retention capacity, it is advisable to line them using appropriate waterproofing techniques. Similarly, the selection of basin installation sites should consider the soil characteristics and site-specific considerations. The findings of this research highlight the potential of runoff water harvesting basins to significantly improve agricultural productivity and resilience in the West African Sahel, thereby contributing to enhanced food security and improved livelihoods for local farmers. Full article

Sahelian countries including Burkina Faso face multiple challenges related to climatic conditions. Setting up effective disaster management plans is essential for protecting livelihoods and promoting sustainable development. Soil and water conservation measures (SWCMs) are emerging as key components of such plans, particularly in Burkina Faso. However, there is an insufficiency of studies exploring their potential as green infrastructures in the Sahelian context and this research aims to contribute to filling this gap. We used national data, remote sensing, and GIS tools to assess SWCM adoption and the potential for climate resilience. Stone ribbons emerged as the most widely adopted SWCM, covering 2322.4 km² especially in the northern regions, while filtering dikes were the least widely adopted, at 126.4 km². Twenty years of NDVI analysis showed a notable vegetation increase in Yatenga (0.075), Oudalan (0.073), and provinces with a high prevalence of SWCM practices. There was also an apparent increase in SWCM percentages from 60% of land degradation. Stone ribbons could have led to a runoff reduction of 13.4% in Bam province, highlighting their effectiveness in climate resilience and flood risk mitigation. Overall, encouraging the adoption of SWCMs offers a sustainable approach to mitigating climate-related hazards and promoting resilience in Sahelian countries such as Burkina Faso. Full article

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article

The area of Sia Kouanza in the Sahel of southwestern Niger is a potential location for expanding agriculture through irrigation with groundwater. Agriculture is key to supporting smallholders and promoting food security. As plans proceed, questions include how much water is available, how is groundwater replenished, many hectares to develop, and where to locate the wells. While these questions can be addressed with a model, it is difficult to find detailed procedures, especially when data are scarce. How can we use existing information to develop a model of a natural system where groundwater development will take place? We describe an approach that can be employed in data-scarce areas where similar questions are being asked. The approach includes setting details; conceptual model development; water balance; numerical code MODFLOW; model construction, calibration, and statistics; and result interpretation. Conceptual model component estimates are derived from field data: recharge, evapotranspiration, wetlands discharge, existing extraction, and river stages. When field data are not available or scarce, we employ other sources and describe how they are validated with field data or analog sites. The calibrated steady-state model gives a water balance of 22 × 10⁶ m³/yr with inflows (recharge 22 × 10⁶ m³/yr) and outflows (extraction 7.2 × 10⁵ m³/yr, wetlands 5.7 × 10⁶ m³/yr, evapotranspiration 11.9 × 10⁶ m³/yr). The model is a point of departure; approaches for transient and predictive models, which can be used to simulate changes in irrigation pumping volumes and drought, for example, will be described subsequently. Full article

This study evaluates the impact of sub-daily bias correction of gridded rainfall products (RPs) on the estimation rainfall erosivity in Burkina Faso (West African Sahel). Selected RPs, offering half-hourly to hourly rainfall, are assessed against 10 synoptic stations over the period 2001–2020 to appraise their accuracy. The optimal product (the integrated multi-satellite retrievals for GPM, IMERG) is further used as a reference for bias correction, to adjust the rainfall distribution in the remaining RPs. RPs-derived rainfall erosivity is compared to the global rainfall erosivity database (GloREDa) estimates. The findings indicate that bias correction improves the rainfall accuracy estimation for all RPs, in terms of quantitative, categorial metrics and spatial patterns. It also improved the distributions of rainfall event intensities and duration across all products, which further significantly improved the annual rainfall erosivity estimates at various timescales along with spatial patterns across the country, as compared to raw RPs. The study also highlights that bias correction is effective at aligning annual trends in rainfall with those in rainfall erosivity derived from RPs. The study therefore underscores the added value of bias correction as a practice for improving the rainfall representation in high-resolution RPs before long-term rainfall erosivity assessment, particularly in data-scarce regions vulnerable to land degradation. Full article

Increased dust emissions from dryland areas and their effects on human health, ecosystem viability, and environmental change are a global concern in the face of the growing climate crisis. Dust plume emissions from the West African landmass, Sahara, and Sahel areas comprise a major fraction of the global aerosol budget. Dust plume intensity is closely related to regional winds (e.g., Harmattan, Sahara Air Layer), the Intertropical Convergence Zone, monsoonal seasonality, marine currents, and physiography. To study terrigenous material emitted from the continent over the last ~260 kyr (late Quaternary), we used X-ray fluorescence spectroscopy (XRF) to analyze a ~755 cm long marine sediment core from the eastern equatorial Atlantic Ocean, resulting in nearly 1400 discrete measurements. Spectral analysis results suggest that concentrations of elements (Rb, Sr, Si, Al) preserved in the sediments are correlated to different types of orbital climate forcing. Chemical weathering intensity indicated by the Rb/Sr ratio was sensitive to seasonal insolation variations controlled by precession cycles (23–18 kyr), which presumably reflects the relationship between monsoonal rainfall and sensible heating of the continent. Spectral analysis of silicate mineral grain size (Si/Al) showed significant 40 kyr cycles that were paced by obliquity. Based on these data, we infer that winter tradewind activity accelerated in response to the intertropical insolation gradient induced by high obliquity. High Rb/Sr ratios during the last glacial maximum and penultimate glacial maximum may have been due to a predominance of mechanical weathering over chemical weathering under dry/cool climates or the dissolution of Sr-bearing carbonates by corrosive glacial bottom waters. Full article

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada’s extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km² and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country. Full article