scholarly journals Slope and distance from buildings are easy-to-retrieve proxies for estimating livestock site-use intensity in alpine summer pastures

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259120
Author(s):  
Marco Pittarello ◽  
Simone Ravetto Enri ◽  
Michele Lonati ◽  
Giampiero Lombardi
Keyword(s):  
Linear Models ◽  
Regional Scale ◽  
Open Data ◽  
Livestock Grazing ◽  
Data Sources ◽  
European Alps ◽  
Ecological Groups ◽  
Intensity Gradient ◽  
Site Use ◽  
Farm Scale

Regardless of the issue, most of the research carried out on summer pastures of European Alps had to consider the effects of grazing management, as it is an intrinsic component of alpine environment. The management intensity of grazing livestock is measured in terms of livestock stocking rate, but not always a direct measure of it is easily retrievable. Therefore, the aim of the research was to test the reliability of proxies easily retrievable from open data sources (i.e. slope and distance from buildings) in approximating the pastoral site-use intensity. To test the proxies’ effectiveness two different approaches were used. With the first one, the proxies’ reliability was assessed in a case-study conducted at farm scale by using the number of positions gathered with GPS collars, which are a reliable measure of livestock site-use intensity. With the second, the proxies’ reliability was assessed by means of five Vegetation Ecological Groups (VEGs), used as a tool for indirect quantification of livestock site-use intensity at regional scale (thirty-two alpine valleys of the Western Italian Alps, Piedmont Region—Italy). At farm scale, distance from buildings and slope were both reliable predictors of the number of GPS locations as assessed with a Generalized Additive Model. Results of Generalized Linear Models at the regional scale showed that the values of both the slope and the distance from buildings were able to separate VEGs along the same site-use intensity gradient assessed by modelling the number of GPS locations at farm scale. By testing proxies’ reliability both with a direct (i.e. GPS collar positions) and indirect (i.e. VEGs) measurement of livestock site-use intensity, results indicated that slope and distance from buildings can be considered effective surrogates of site-use intensity gradient in alpine grasslands managed under livestock grazing. Therefore, when the level of site-use intensity in research carried out in alpine summer pastures is not directly available, a reliable solution consists in the use of the terrain slope and the distance from buildings, which are also easily retrievable from open data sources or computable.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals Agriculture and Supporting Ecosystem Services: A Trade-off Analysis Using Open Data in Brazil

2019 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Ana Paula Dias Turetta ◽  
Bruno Pedrosa ◽  
Luca Eufemia ◽  
Michelle Bonatti ◽  
Stefan Sieber
Keyword(s):  
Ecosystem Services ◽  
Service Provision ◽  
Agricultural Production ◽  
Methodological Approach ◽  
Regional Scale ◽  
Open Data ◽  
Sustainable Land Use ◽  
Production Region ◽  
Trade Offs ◽  
Agricultural Areas

Open data are important for adding legitimacy and transparency to public sciences. These data have also a potential to be used as a first approach for scientific investigation, such as spatial evaluation of ecosystem services. This paper presents a methodological approach to evaluate the trade-offs between agriculture and supporting ecosystem services based on spatial analysis and open data. The study area is an important agricultural production region in Bahia State, Brazil. The framework was able to establish the spatial interactions between agriculture and ecosystem service provision, while the regional scale was useful in supporting guidelines regarding sustainable land use for agricultural areas.

Managing Large Volumes of Interlinked Text and Knowledge With the KnowledgeStore

2018 ◽  
pp. 32-61
Author(s):  
Francesco Corcoglioniti ◽  
Marco Rospocher ◽  
Roldano Cattoni ◽  
Bernardo Magnini ◽  
Luciano Serafini
Keyword(s):  
Semantic Web ◽  
Open Source ◽  
Fault Tolerant ◽  
Storage System ◽  
Open Data ◽  
Unstructured Data ◽  
Data Sources ◽  
Concept Design ◽  
Design Function ◽  
Eu Project

This chapter describes the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system to jointly store, manage, retrieve, and query interlinked structured and unstructured data, especially designed to manage all the data involved in Knowledge Extraction applications. The chapter presents the concept, design, function and implementation of the KnowledgeStore, and reports on its concrete usage in four application scenarios within the NewsReader EU project, where it has been successfully used to store and support the querying of millions of news articles interlinked with billions of RDF triples, both extracted from text and imported from Linked Open Data sources.

scholarly journals Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 474 ◽  
Author(s):  
Karl Adler ◽  
Kristin Piikki ◽  
Mats Söderström ◽  
Jan Eriksson ◽  
Omran Alshihabi
Keyword(s):  
Cross Validation ◽  
Linear Models ◽  
Prediction Models ◽  
National Scale ◽  
X Ray ◽  
Fluorescence Measurements ◽  
Multivariate Modelling ◽  
Farm Scale ◽  
Adaptive Regression ◽  
Mlr Model

Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R2 values for predictions of Cu (R2 = 0.63), Zn (R2 = 0.92), and Cd (R2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R2 = 0.94) and Cd (R2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.

scholarly journals Large regional-scale variation in C3/C4 distribution pattern of Inner Mongolia steppe is revealed by grazer wool carbon isotope composition

2009 ◽  
Vol 6 (5) ◽  
pp. 795-805 ◽  
Author(s):  
K. Auerswald ◽  
M. H. O. M. Wittmer ◽  
T. T. Männel ◽  
Y. F. Bai ◽  
R. Schäufele ◽  
...  
Keyword(s):  
Carbon Isotope ◽  
Inner Mongolia ◽  
Isotope Composition ◽  
Regional Scale ◽  
C4 Plants ◽  
Entire Area ◽  
Community Biomass ◽  
Farm Scale ◽  
Sheep Wool ◽  
Scale Variation

Abstract. This work explored the spatial variation of C3/C4 distribution in the Inner Mongolia, P. R. China, steppe by geostatistical analysis of carbon isotope data of vegetation and sheep wool. Standing community biomass (n=118) and sheep wool (n=146) were sampled in a ~0.2 Mio km2 area. Samples from ten consecutive years (1998–2007) were obtained. Community biomass samples represented the carbon isotopic composition of standing vegetation on about 1000 m2 ("community-scale"), whereas the spatio-temporal scale of wool reflected the isotope composition of the entire area grazed by the herd during a 1-yr period (~5–10 km2, "farm-scale"). Pair wise sampling of wool and vegetation revealed a 13C-enrichment of 2.7±0.7‰ (95% confidence interval) in wool relative to vegetation, but this shift exhibited no apparent relationships with environmental parameters or stocking rate. The proportion of C4 plants in above-ground biomass (PC4, %) was estimated with a two-member mixing model of 13C discrimination by C3 and C4 vegetation (13Δ3 and 13Δ4, respectively), in accounting for the effects of changing 13C in atmospheric CO2 on sample isotope composition, and of altitude and aridity on 13Δ3. PC4 averaged 19%, but the variation was enormous: full-scale (0% to 100%) at community-scale, and 0% to 85% at farm-scale. The farm-scale variation of PC4 exhibited a clear regional pattern over a range of ~250 km. Importantly PC4 was significantly higher above the 22°C isotherm of the warmest month, which was obtained from annual high-resolution maps and averaged over the different sampling years. This is consistent with predictions from C3/C4 crossover temperature of quantum yield or light use efficiency in C3 and C4 plants. Still, temperature gradients accounted for only 10% of the farm-scale variation of PC4, indicating that additional factors control PC4 on this scale.

scholarly journals Analyzing the Performances of Water User Associations to Increase the Irrigation Sustainability: An Application of Multivariate Statistics to a Case Study in Italy

2020 ◽  
Vol 12 (16) ◽  
pp. 6327
Author(s):  
Demetrio Antonio Zema ◽  
Pasquale Filianoti ◽  
Daniela D’Agostino ◽  
Antonino Labate ◽  
Manuel Esteban Lucas-Borja ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Linear Models ◽  
Regional Scale ◽  
Water Price ◽  
Water User Associations ◽  
Large Variability ◽  
Water User ◽  
Economy Of Scale ◽  
Self Sufficiency

Benchmarking techniques are useful and simple tools to analyze the performance of the collective irrigation in the Water User Associations (WUAs) towards an increase in service sustainability. Several benchmarking techniques have been proposed to process and predict performance indicators. Instead, some meaningful statistical techniques based on the distance of data samples, which overcome the limitations of the traditional benchmarking techniques, have never been applied to the collective irrigation sector. This study applies Permutational Multivariate Analysis of Variance (PERMANOVA), Multidimensional Scale Models (MDS), and Distance-Based Linear Models (DISTLM) as benchmarking techniques to evaluate the technical and financial performances of 10 WUAs in Calabria (Southern Italy). These benchmarking techniques revealed that the significant differences in the irrigated areas and financial self-sufficiency of the WUAs, shown by PERMANOVA, depend on the large variability of the remaining performance indicators. Both the MDS and DISTLM demonstrated that a higher number of associated users and larger irrigation service coverage allows an increase in the irrigated areas; this enlargement is facilitated if the water price and the size of the personnel staff decrease. The WUAs’ self-sufficiency is mainly influenced by the number of workers and the maintenance, organization, and management costs, while the impacts of the due service fees and water price are more limited; it is also convenient to increase the number of the associated farmers since this increases the economy of scale and the gross revenues of the irrigation service. Overall, from the analysis carried out for the regional case study, these benchmarking techniques seem to be powerful and easy tools to identify the problems of the irrigation service and help in planning the most suitable policies to improve the sustainability of the collective irrigation at the regional scale.

A farm-scale framework to assess potential farm- and regional-scale implications of removing palm-kernel expeller as a supplementary feed for dairy cows

2017 ◽  
Vol 57 (7) ◽  
pp. 1336 ◽  
Author(s):  
Ronaldo Vibart ◽  
Alec Mackay ◽  
Andrew Wall ◽  
Iris Vogeler ◽  
Josef Beautrais ◽  
...  
Keyword(s):  
Production Systems ◽  
Regional Scale ◽  
Palm Kernel ◽  
Dairy Farms ◽  
Barley Grain ◽  
N Leaching ◽  
Pasture Production ◽  
Farm Scale ◽  
Supplementary Feed ◽  
The Impact

Farm-scale models were integrated with spatially discrete estimates of pasture production to examine the potential farm and regional implications of removing palm-kernel expeller (PKE) as a supplementary feed from dairy farms in Southland, New Zealand. The following two farm-production systems representing the majority of dairy farms in the region were modelled: a System 3 farm (D3; mid-intensification, with 10–20% of imported feed) and a System 4 farm (D4; mid- to high intensification, with 20–30% of imported feed). Within each system, the impact of the following four PKE options was explored: (1) a control with PKE (Baseline); (2) no PKE, with fewer cows producing the same amount of milk per cow as in Baseline; (3) no PKE, with the same number of cows producing less milk per cow than in Baseline; and (4) PKE replaced with barley grain. Barley grain provides for similar flexibility (timing of purchase and feeding), and can be sourced locally. Faced with the need to remove PKE as a dietary ingredient, farmers would benefit from adopting the second PKE option (no PKE, with fewer cows producing the same amount of milk per cow as in Baseline); farm-operating profits were reduced by only 3% (compared with 30% of System 4 farms adopting the third PKE option, i.e. no PKE, with the same number of cows producing less milk per cow than in Baseline) relative to the Baseline farms. The narrow range of mean annual nitrate-nitrogen (nitrate-N) leaching losses (estimates ranged from 30 to 33 kg N/ha) reflects similar estimates of N intake and N excreted in urine across the modelled options. Substantial amounts of barley grain would need to be transported into the region or produced locally to replace PKE.

scholarly journals The CoBiS Linked Open Data Project and Portal

2018 ◽  
Vol 186 ◽  
pp. 12013 ◽  
Author(s):  
Luisa Schiavone ◽  
Federico Morando ◽  
Keyword(s):  
Open Data ◽  
Heterogeneous Data ◽  
Data Sources ◽  
External Data ◽  
Heterogeneous Data Sources ◽  
Historical Archives ◽  
Technical Realization ◽  
Metrological Research ◽  
Academy Of Sciences ◽  
Mapping Language

The CoBiS is a network formed by 65 libraries. The project is a pilot for Piedmont that is aiming to provide the Committee with an infrastructure for LOD publishing, thus creating a triplification pipeline designed to be easy to automate and replicate. This is being realized with open source technologies, such as the RML mapping language or the JARQL tool that uses Linked Data to describe the conversion of XML, JSON or tabular data into RDF. The first challenge consisted in making possible the dialog of heterogeneous data sources, coming from four different library software (Clavis, Erasmo, SBNWeb and BIBLIOWin 5.0web) and different types of data (bibliographic, multimedia, and archival). The information contained in the catalogs is progressively interlinked with external data sources, such as Wikidata, VIAF, LoC and BNF authority files, Wikipedia and the Dizionario Biografico degli Italiani. Partners of the CoBiS LOD Project are: National Institute for Astrophysics (INAF), Turin Academy of Sciences, Olivetti Historical Archives Association, Alpine Club National Library, Deputazione Subalpina di Storia Patria, National Institute for Metrological Research (INRIM). The technical realization of the project is entrusted to Synapta, and it is partially sponsored by Piedmont Region.

scholarly journals Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

2018 ◽  
Vol 10 (11) ◽  
pp. 1757 ◽  
Author(s):  
Sarah Asam ◽  
Mattia Callegari ◽  
Michael Matiu ◽  
Giuseppe Fiore ◽  
Ludovica De Gregorio ◽  
...  
Keyword(s):  
Snow Cover ◽  
Regional Scale ◽  
Temporal Trends ◽  
Earth Observation ◽  
European Alps ◽  
Climate Variables ◽  
South Tyrol ◽  
Mean Temperature ◽  
The Alps ◽  
The Relationship

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.

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