Remote sensing-aid assessment of wetlands in central Malawi

Author(s):  
Emmanuel Ogunyomi ◽  
Byongjun Hwang ◽  
Adrian Wood

<p>Many areas in Malawi undergo extreme seasonality: floods in the wet season and drought in the dry season. Each year, this extreme seasonality poses formidable challenges for local farmers to sustain their crops. Often in the dry season, farmers use the water in the surrounding seasonal wetlands (dambos) for small-scale irrigation to supplement their rainy season harvest. In Malawi, the agricultural use of wetland is growing year by year and these areas play significant roles in regulating food price shocks and price. Such intensive use of wetlands can negatively affect the sustainability of wetland eco-system and their crop production, with communities even affected by the drying up of wells. Farmers, especially small-scale farmers, will face even more challenges for sustaining wetland production, as climate changes cause more frequent occurrence of droughts as Malawi has experienced in recent years. With the increasingly intensive use of these seasonal wetlands for agricultural purpose and the expansion of wetland degradation generally across the country, more attention is required toward effective management of these wetlands through identification, mapping, monitoring and data analysis. To achieve the sustainable use of these seasonal wetlands, it is essential to establish systematic monitoring and assessment procedures. Widely used assessment protocols (i.e., WET-Health) which evaluate the wetlands based on physical indicators such as land cover, hydrology, geomorphology, soil organic matter and natural vegetation have been successfully implemented in South Africa. However, obtaining those indicators across the full length of an individual wetland, let alone all wetlands in one district in Malawi, is labour intensive and time-consuming and difficult to complete. In this research, we utilise both unmanned aerial vehicle (UAV) and satellite imageries. These data sources are being tested in nine different seasonal wetlands in central Malawi to provide an accurate derivation of key indicators such as gully formation, sedimentation, water extent, changes in land use and natural vegetation. Additionally, using satellite imageries and GIS, the condition of each individual wetland has been quantified, with land cover and the extent of inundation determined through multi-temporal data analysis. Our results can be applied across a larger area, i.e. several districts to help identify where more detailed ground assessment is needed and technical support required to improve wetland management, feeding into both policy and technical guidance which can help sustain the range of ecosystems services of these important areas.</p>

2019 ◽  
Author(s):  
Rumen Manolov

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.


EcoHealth ◽  
2021 ◽  
Author(s):  
Felipe A. Hernández ◽  
Amanda N. Carr ◽  
Michael P. Milleson ◽  
Hunter R. Merrill ◽  
Michael L. Avery ◽  
...  

AbstractWe investigated the landscape epidemiology of a globally distributed mammal, the wild pig (Sus scrofa), in Florida (U.S.), where it is considered an invasive species and reservoir to pathogens that impact the health of people, domestic animals, and wildlife. Specifically, we tested the hypothesis that two commonly cited factors in disease transmission, connectivity among populations and abundant resources, would increase the likelihood of exposure to both pseudorabies virus (PrV) and Brucella spp. (bacterial agent of brucellosis) in wild pigs across the Kissimmee Valley of Florida. Using DNA from 348 wild pigs and sera from 320 individuals at 24 sites, we employed population genetic techniques to infer individual dispersal, and an Akaike information criterion framework to compare candidate logistic regression models that incorporated both dispersal and land cover composition. Our findings suggested that recent dispersal conferred higher odds of exposure to PrV, but not Brucella spp., among wild pigs throughout the Kissimmee Valley region. Odds of exposure also increased in association with agriculture and open canopy pine, prairie, and scrub habitats, likely because of highly localized resources within those land cover types. Because the effect of open canopy on PrV exposure reversed when agricultural cover was available, we suggest that small-scale resource distribution may be more important than overall resource abundance. Our results underscore the importance of studying and managing disease dynamics through multiple processes and spatial scales, particularly for non-native pathogens that threaten wildlife conservation, economy, and public health.


Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 246
Author(s):  
Markose Chekol Zewdie ◽  
Michele Moretti ◽  
Daregot Berihun Tenessa ◽  
Zemen Ayalew Ayele ◽  
Jan Nyssen ◽  
...  

In the past decade, to improve crop production and productivity, Ethiopia has embarked on an ambitious irrigation farming expansion program and has introduced new large- and small-scale irrigation initiatives. However, in Ethiopia, poverty remains a challenge, and crop productivity per unit area of land is very low. Literature on the technical efficiency (TE) of large-scale and small-scale irrigation user farmers as compared to the non-user farmers in Ethiopia is also limited. Investigating smallholder farmers’ TE level and its principal determinants is very important to increase crop production and productivity and to improve smallholder farmers’ livelihood and food security. Using 1026 household-level cross-section data, this study adopts a technology flexible stochastic frontier approach to examine agricultural TE of large-scale irrigation users, small-scale irrigation users and non-user farmers in Ethiopia. The results indicate that, due to poor extension services and old-style agronomic practices, the mean TE of farmers is very low (44.33%), implying that there is a wider room for increasing crop production in the study areas through increasing the TE of smallholder farmers without additional investment in novel agricultural technologies. Results also show that large-scale irrigation user farmers (21.05%) are less technically efficient than small-scale irrigation user farmers (60.29%). However, improving irrigation infrastructure shifts the frontier up and has a positive impact on smallholder farmers’ output.


2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.


2014 ◽  
Vol 2014 ◽  
pp. 1-52
Author(s):  
Bin Mushambanyi Théodore Munyuli

A study was conducted from 2010 to 2012 around the flower growing areas in central Uganda to generate baseline information on the status of pollinators. Primary data were gathered using a questionnaire that aimed at determining farmers and flower farm officials’ perceptions on the impact of activities carried out inside greenhouses on pollinators, human health, and on crop production in the surroundings. Results indicated that the quantity of pesticides and fertilizers applied daily varied among the different flower farms visited. Bee species richness and abundance varied significantly (P<0.01) according to flower farm location, to the landscape vegetation type, and to field types found in the surrounding of flower farms. Bee richness found around flower farms varied in number from 20 to 40 species in total across seasons and years. Bee density increased significantly with the increase in flower density. Small-scale farmers were aware of the value and importance of pollination services in their farming business. There was no clear evidence of a direct effect of agrochemicals application on bee communities living in the surrounding habitats. There is a need for further research to be conducted on human health risks and for toxicological studies on soils, plants, flowers, and bees in the farm landscape.


2006 ◽  
Author(s):  
K.J. Choe ◽  
K.Y. Oh ◽  
B.K. Yu ◽  
S.H. Lee ◽  
K.J. Choe ◽  
...  

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