Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Hydrogeochemical characterization and assessment of factors controlling groundwater salinity in the Chamwino granitic complex, central Tanzania

Chamwino district, central Tanzania is a semi-arid granitic complex province, where groundwater is the major source of water for domestic and other uses. However, groundwater in the area is affected by salinity, thus, lowering the availability of potable water for various uses, decrease in crop production, taste less, wastage of soap, and abnormal pain. Due to this, this study sought to characterize groundwater using hydrogeochemical facies and signatures in order to identify the factors influencing the distribution of salt water in the Chamwino Granitic Complex. A total of 141 groundwater samples were collected from wells spatially distributed within the study area from January 2023 to April 2023, (a season of relatively low rainfall). All samples were subjected to in situ analyses of physicochemical parameters pH, temperature (T), total dissolved solids (TDS), electrical conductivity (EC), and salinity using a multi-parameter water analyzer and analyses of major ions (Ca2+, Mg2+, K+, Na+, Cl-, SO42-, HCO3-, and NO3-). The study revealed that the dominant cations in the groundwater are Na+ > Ca2+ > Mg2+, and the anions are Cl- > HCO3- > SO42. Five geological formations (granodiorite, tonalitic orthogenesis, migmatite, tonalite, and alluvium) were identified, and each is characterized by its unique groundwater facie. In the areas that are dominated with granodiorite, the major hydrogeochemical facies were Ca-HCO3, Na-Cl, Ca-Na-HCO3, Ca-Mg-Cl, and Ca-Cl water types; tonalitic orthogenesis was dominated by Ca-HCO3, Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; migmatite was dominated by Ca-HCO3, Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; tonalite was dominated by Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; and alluvium was dominated by Na-Cl and Ca-Mg-Cl and Ca-Cl water types. The common hydrogeochemical facies in all five geological units are Na-Cl, Ca-Mg-Cl, and Ca-Cl water types. It is revealed that the groundwater in the study area is alkaline in nature and slightly saline with salinity level between 0.2 mg/L (fresh water) and 2.8 mg/L (brackish water) with mean 1.07 mg/L (of 141 samples). The factors controlling groundwater salinity distribution are mainly rock-water interaction and ion exchange reactions. Groundwater salinity in the study area is largely attributed to the abundance of Na+, Ca2+, Cl- and SO42-. Abundance of Na+ and Ca2+ is the results of both, weathering of feldspar minerals particularly plagioclase (Na-Ca feldspars) which are the major mineral in granites, and evaporation crystallization cycles of evaporates in semi-arid areas such as Chamwino. Also, such evaporation crystallization cycles account for the abundance of Cl- and SO42- especially in areas dominated by alluvium. However, anthropogenic activities as evidenced by elevated nitrate up to 212.6 mg/L in congested areas are also likely to contribute in area) to the elevated Cl- and SO42-. In other geological units such as tonalitic orthogneiss, migmatite and granodiorite, there was an ostensible mixing of saline water with fresh water from local recharge as indicated by the abundance of HCO3- ions. Nonetheless, the hydrogeochemical characterization of groundwater in the Chamwino granitic complex suggests that there is little possibility for groundwater to evolve to a carbonate water type (fresh water) because the groundwater salinity is mainly geogenic, unless artificial recharge through rainwater harvesting is applied.

[Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China]

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

Predicting the future land use and land cover changes for Saroor Nagar Watershed, Telangana, India, using open-source GIS

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

QGIS-based weighted linear combination plugin for landfill site selection: a case study in Tokat Province, Turkey

Proper disposal of solid waste is crucial for the protection of natural resources and human health. However, increasing population and changes in consumption habits have led to a global increase in solid waste production. Therefore, a site selection process for solid waste management that takes into account environmental, economic, and social factors is needed. The number of open-source GIS (geographic information system) software programs used in site selection analysis is increasing day by day. QGIS software is an open-source GIS software developed by free software developers, with its popularity increasing with each new version and allowing for the development of plugins with the Python programming language. The shareability of plugins developed with QGIS software brings together open-source GIS users around the world for common goals. In this study, a plugin called "LANDFILL SITE SELECTION (LFSS)" was developed in the QGIS software environment for solid waste landfill site selection and a suitability map was created for solid waste landfill site selection in Tokat, Turkey, using this plugin. For this purpose, 14 evaluation criteria and 8 exclusion criteria were selected, the importance levels of criteria and sub-criteria were determined using the AHP method, and a solid waste landfill site selection suitability map was created using the developed plugin.

Suitable municipalities for biomass energy use in Colombia based on a multicriteria analysis from a sustainable development perspective

Using renewable energies is a global strategy to mitigate the acceleration of global warming generated by industrial processes and is a sustainable way to diversify the energy matrix in all countries. Biomass is a renewable energy source that produces biofuels and generates electricity and heat. The primary purpose of this work is to identify the municipalities in Colombia where agricultural, livestock, and urban residual biomass could be suitable for energy generation in a sustainable and renewable way. To that end, we carried out a Geostatistical Multi-Criteria Decision Methodology using Analytical Hierarchy Processes such as Rank-Sum and Weighted Linear Combination, as well as considering a set of sustainable development indicators applied to official Colombian data. Two scenarios are considered for comparison purposes. The first one is according to expert criteria, and the second one considers The Sustainable Development Goals proposed by the United Nations. Under both proposed scenarios, 127 municipalities were found to be suitable for agricultural-urban residual biomass and 162 for livestock-urban residual biomass for energy generation. One of the main limitations for the use of urban biomass is that municipalities need to have sufficient production potential to fulfill their own energy needs. An additional comparison with previous works to evaluate the performance of the Multi-Criteria Decision Methodologies MCDM is also proposed.

Assessment of influencing level of rainfall and physical factors on groundwater level for a semi-arid flat terrain watershed using grid-based geospatial analysis: a case study from Lower Palar Basin, Tamil Nadu, India

Understanding natural phenomena with the help of modern scientific approaches helps to reach sustainable solutions for current and future water-related problems. In this context, present study aims to assess relative influencing level of physical factors in controlling groundwater level, using a novel grid-based delineation technique, in Lower Palar River Basin, in Kanchipuram and Chengalpattu districts of South India. The influencing factors viz-a-viz: rainfall, soil texture, land use/land cover, terrain slope, geomorphology, lithology, and drainage characteristics were considered for the study. Archived data (2011 to 2020) of monthly rainfall at four rain gauge stations and monthly groundwater level of 22 locations, soil texture, lithology, and geomorphology data were considered for the study. SRTM digital elevation model with 30-m resolution was used for analyzing drainage characteristics and terrain slope. Thematic maps for considered factors were prepared, using common grid delineation method in GIS platform that divided study area into 52 grids, to inter-relate the discrete and continuous parameters with groundwater level. Results indicate that level of influence increases in the order of precipitation followed by lithology, land use/land cover, terrain slope, geomorphology, infiltration number, and soil texture. The study shows groundwater resilience is highly influenced by soil texture and infiltration number compared to other factors considered. It can be concluded that grid-based delineation successfully identifies grids with significant influence of individual factors by comparing with groundwater resilience. Common grid-based delineation method proves to be more effective in assessing groundwater resilience and can be used more efficiently in groundwater studies.

LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area

In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source JavaScript libraries (i.e., Leaflet, jQuery, Chroma, Geostats, DataTables, and ApexChart) to allow public health researchers the ability to map, download, and chart 21 publicly available datasets for clinical, educational, and epidemiological audiences. County and census tract data used in choropleth maps were all downloaded from the sources website and linked to Pennsylvania and catchment area county and census tract geographies, using a QGIS plugin and Leaflet JavaScript. Two LionVu demonstrations are presented, and 10 other public health related web-GIS applications are reviewed. LionVu fills a role in the public health community by allowing clinical, educational, and epidemiological audiences the ability to visualize and utilize health data at various levels of aggregation and geographical scales (i.e., county, or census tracts). Also, LionVu is a novel application that can translate and can be used, for mapping and graphing purposes. A dialog to demonstrate the potential value of web-based GIS to a wider audience, in the public health research community, is needed.

Estimating geothermal and background radiation hotspots from primordial radionuclide concentrations in geology of South Africa

Naturally occurring radionuclides are the main generator of geothermal energy in the Earth's crust and mantle. The generated energy is consequently directly proportional to the concentrations of the three main naturally occurring radionuclides (uranium, thorium and potassium), which are primordial in origin. Concentrations of these naturally occurring radionuclides were extracted for all the different geological rock units in South Africa. The radionuclide concentrations were then mapped and integrated by using QGIS. The results were used to estimate and map the geothermal energy production rates for the rock units. The radionuclide concentrations in the rock units were also used to identify regions with high radiation background. These radiation hotspots were plotted and investigated. The estimated geothermal energy and background radiation hotspots were compared to measurements and projections of other studies and good corelations were found.

Association of tobacco use with the tobacco-related built environment: an ecological study from urban slums of Bhopal, India

INTRODUCTION

Tobacco is one of the biggest public health problems and a major risk factor for various non-communicable diseases (NCDs). An important aspect of tobacco control strategy could include modifications in the tobacco-related built environment. This study investigated the association between tobacco shop density and tobacco use prevalence in the urban slums of Bhopal city, India.

METHODS

We conducted a cross-sectional survey to obtain the distribution of tobacco-related built environment (tobacco shops) in the neighbourhood (400-m service area) of 32 urban slum clusters of Bhopal. We plotted this distribution using the 'network service area analysis' in ArcMap 10.7.1 software. Then, we used an ecological design to determine the association between tobacco shop density and tobacco use prevalence in these 32 clusters (N = 6214 adult inhabitants). We used multiple linear regression analysis to estimate the regression coefficient (adjusted for socio-demographic variables) between tobacco use and tobacco shop density at the cluster level.

RESULTS

The prevalence of tobacco use among all 32 slum clusters ranged from 22.1 to 59.6% (median 40.9% with IQR 31.8-44.2). There were 194 tobacco shops situated in the neighbourhood of all clusters. The median density of tobacco shops was 59.40/km² (IQR 39.9-108.1/km²) in the neighbourhoods of slum clusters. Tobacco use prevalence was significantly associated with tobacco shop density (estimate or B = 0.071, p value = 0.002) after adjusting for age, literacy, wealth index, and gender ratio.

CONCLUSIONS

Tobacco use prevalence is significantly associated with tobacco shop density in the slums of Bhopal city in central India. We need to develop appropriate built environment interventions to control rampant tobacco use.

Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil

Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r²) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8 °C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.

Detection of Water Spread Area Changes in Eutrophic Lake Using Landsat Data

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann-Kendall test was used. San's slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake's water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions.

Adaptation capability of rainfall hotspots in water resilient cities using QGIS: a case study of Taichung City in Taiwan

In the context of extreme climate due to global climate transition, rainwater adaptation in resilient cities is a key issue for countries. The purpose of this study is to identify the rainfall hotspots in urban areas and investigate whether these hotspots have environmental conditions for rainfall adaptation. The study site is located in the Taichung area. This study collects rainfall data from rainfall stations at elevations below 500 m, employs QGIS (quantum GIS) to create an inverse distance weighted graphical distribution of rainfall to determine the hotspots where the maximum and minimum rainfalls occur, identifies the topography, green spaces, water areas, and buildings in the catchment, integrates the coverage area in the project, and estimates the amount of rainwater that could be directly absorbed by the land within the catchment. The results of this study show that, among the rainfall stations at an elevation below 100 m where most urban areas are located, the Taichung rainfall station is the area with the highest number of rainfall events from May to August. Without reliance on gully or river drainage, the natural infiltration of the land in the catchment could only adjust to 80 mm of heavy precipitation within 24 h of the rainfall warning level of the Central Weather Bureau.

Mass grave localization prediction with geographical information systems in Guatemala and future impacts

Conducting physical searches for mass grave locations based on anecdotal evidence is a time-consuming and resource-intensive endeavor in circumstances that often pose a threat to personal safety. The development of tools and procedures to speed such searches can greatly reduce the risk involved, increase the number of individuals whose remains are recovered and identified, and more importantly, reunite these remains with their loved ones to provide them with a proper burial. Geographic information systems (GIS) software, which can analyze and manipulate the spatial characteristics of known mass grave data, represents a powerful tool that can be used to predict new mass grave locations and increase the speed and efficiency with which they are investigated. Using the open source QGIS project, existing mass grave locations in Guatemala were analyzed based on their distance from and change in elevation relative to roads, streets, waterways, points of interest, and possible villages/towns. Statistical and geostatistical analyses performed to detect relationships among the variables resulted in patterns that warrant further study and can be used to further narrow areas of investigation.

Estimating indoor radon concentrations based on the uranium content of geological units in South Africa

Very few studies have been done on radon in South Africa, even though South Africa holds nearly a tenth of the global uranium deposits. This study aimed to map and estimate the radon risk for South Africa, and to identify potential hotspots. In this study, the uranium content of the different types of rock was determined and uranium concentrations in geological units were then projected. A uranium distribution map of South Africa was then constructed, and indoor radon concentrations were estimated and mapped based on the uranium levels of areas. Towns in areas where indoor radon measurements were conducted compared well with the estimated radon values. The maps predicted high estimated indoor radon concentrations in areas at several geological structures. Towns in these areas that have not been measured were identified. The south-western and north-eastern regions of South Africa pose the highest radon risk according to this study, and extensive radon measurements in the towns of these regions is proposed.

Detection of Anaplasma spp. and Bartonella spp. from wild-caught rodents and their ectoparasites in Nakhon Ratchasima Province, Thailand

The objective of this study was to investigate evidence of emerging anaplasmosis and bartonellosis in rodents from endemic areas of Nakhon Ratchasima, Thailand. Rodent trapping was undertaken in 13 sub-districts of Muang District. The live-capture traps were set up in three locations of selected scrub typhus patient houses for three consecutive nights. Wild-caught rodent whole blood samples and associated ticks and fleas were collected and tested for Anaplasma spp. and Bartonella spp. In addition, heat maps using GIS software were used to determine the density of infection of positive wild-caught rodents. A total of 347 wild-caught rodents of nine species was captured. Rattus rattus (38.6%) was the dominant species. A total of 1,518 Heamaphysalis bandicota ticks and 57 Xenopsylla cheopis fleas was removed. Twenty-two of the 347 tested blood samples (6.3%) were Anaplasma bovis-positive and 121 blood samples and five out of 27 pools of X. cheopis fleas were Bartonella queenslandensis-positive. Of these infected rodents, dual-infections between A. bovis and B. queenslandensis were found in three B. indica rodents. Our results offer new information concerning the infections of A. bovis and B. queenslandensis in both rodents and their ectoparasites collected in high-risk areas of rodent-borne diseases in Thailand.

Risk factors for the incursion, spread and persistence of the foot and mouth disease virus in Eastern Rwanda

BACKGROUND

Identification of risk factors is crucial in Foot-and-mouth disease (FMD) control especially in endemic countries. In Rwanda, almost all outbreaks of Foot-and-Mouth Disease Virus (FMDV) have started in Eastern Rwanda. Identifying the risk factors in this area will support government control efforts. This study was carried out to identify and map different risk factors for the incursion, spread and persistence of FMDV in Eastern Rwanda. Questionnaires were administered during farm visits to establish risk factors for FMD outbreaks. Descriptive statistical measures were determined and odds ratios were calculated to determine the effects of risk factors on the occurrence of FMD. Quantum Geographic Information System (QGIS) was used to produce thematic maps on the proportion of putative risk factors for FMD per village.

RESULTS

Based on farmers' perceptions, 85.31% (with p < 0.01) experienced more outbreaks during the major dry season, a finding consistent with other reports in other parts of the world. Univariate analysis revealed that mixed farming (OR = 1.501, p = 0.163, CI = 95%), and natural breeding method (OR = 1.626; p = 0.21, CI = 95%) were associated with the occurrence of FMD indicating that the two risk factors could be responsible for FMD outbreaks in the farms. The occurrence of FMD in the farms was found to be significantly associated with lack of vaccination of calves younger than 12 months in herds (OR = 0.707; p = 0.046, CI = 95%).

CONCLUSIONS

This is the first study to describe risk factors for persistence of FMDV in livestock systems in Rwanda. However, further studies are required to understand the role of transboundary animal movements and genotypic profiles of circulating FMDV in farming systems in Rwanda.

Epidemiological profile and mapping geographical distribution of road traffic accidents reported to a tertiary care hospital, Mangaluru using quantum geographic information system (QGIS)

Background:

The worldwide annual average of road traffic accident (RTA) is approximately 7,00,000 and out of that 10% occur in India. It is estimated that in India, by 2020 RTA would have its fatal effect on about 5,50,000 people annually. This study was conducted to describe the epidemiological profile and spatial distribution of RTAs using quantum geographic information system (QGIS) software reported to a tertiary care hospital in Mangaluru.

Methods:

It was a record based descriptive study conducted in a tertiary care hospital of Mangaluru. The complete enumeration of all RTAs reported to Yenepoya Medical College Hospital (YMCH) during January 2018 to June 2018 was followed. QGIS software was used to depict spatial distribution of the road traffic accident on open street map.

Results:

A total of 180 cases of RTA was reported to the hospital during the study period, of which 86.1% were males. The mean age of the study participants was 33.99 years. The lower limb was the most common site of injury (48.3%) and fractures were the most common type of injury (55.6%). As per the type of RTA majority (55.6%) was motorbike accidents and drivers (47.8%) were the most common RTA victims. Predominantly RTAs occurred during evening hours of the day (40%). QGIS plotting revealed clustering of RTAs in Dakshina Kannada district, North Karnataka and neighboring districts of Kerala.

Conclusion:

QGIS can be used at the health care system level as an important tool to plan preventive measures and early intervention measures at the site of RTA.

GIS-augmented survey of poultry farms with respiratory problems in Haryana

Among various infectious diseases of poultry, diseases of the respiratory tract are responsible for considerable economic losses. The present study was conducted to evaluate some of the risk factors, which included locations of farm, ventilation facilities, number of farms in 1 km² area, agro-climatic zone, and age of flock in relation to respiratory problem in Haryana, India. One hundred poultry flocks with respiratory problems were identified and selected for conducting the survey. The "ODK Collect" app installed on a smartphone was used to capture coordinates of the farms. The collected data was accessed through http://odkproject-iirs.appspot.com/ . The location of farms was mapped with the help of QGIS. All the three parameters, viz., morbidity, mortality (p < 0.001), and case fatality rate (CFR) (p = 0.045), were significantly higher in birds of age 0-2 weeks. Natural ventilation was the most common facility observed in the present study (51/100). Maximum morbidity and mortality were observed in small flocks (< 10,000), whereas maximum CFR was observed in medium-sized flocks (> 10,000-30,000), and there was a significant difference in morbidity, mortality, and CFR. Further, there was a significant difference between agro-climatic zones with respect to morbidity and mortality (p < 0.001). It can be concluded that age, flock size, and agro-climatic conditions have an impact on intensity of diseases especially respiratory diseases. Therefore, special precautions should be taken for young flock. Flock size should be adequate, and such management practices should be adopted that are suitable for particular climatic conditions.

Assessing soil erosion risk using RUSLE through a GIS open source desktop and web application

Soil erosion is a serious environmental problem. An estimation of the expected soil loss by water-caused erosion can be calculated considering the Revised Universal Soil Loss Equation (RUSLE). Geographical Information Systems (GIS) provide different tools to create categorical maps of soil erosion risk which help to study the risk assessment of soil loss. The objective of this study was to develop a GIS open source application (in QGIS), using the RUSLE methodology for estimating erosion rate at the watershed scale (desktop application) and provide the same application via web access (web application). The applications developed allow one to generate all the maps necessary to evaluate the soil erosion risk. Several libraries and algorithms from SEXTANTE were used to develop these applications. These applications were tested in Montalegre municipality (Portugal). The maps involved in RUSLE method-soil erosivity factor, soil erodibility factor, topographic factor, cover management factor, and support practices-were created. The estimated mean value of the soil loss obtained was 220 ton km(-2) year(-1) ranged from 0.27 to 1283 ton km(-2) year(-1). The results indicated that most of the study area (80 %) is characterized by very low soil erosion level (<321 ton km(-2) year(-1)) and in 4 % of the studied area the soil erosion was higher than 962 ton km(-2) year(-1). It was also concluded that areas with high slope values and bare soil are related with high level of erosion and the higher the P and C values, the higher the soil erosion percentage. The RUSLE web and the desktop application are freely available.