15 research outputs found
Soil erosion modelling: A global review and statistical analysis
To gain a better understanding of the global application of soil erosion prediction models, we comprehensivelyreviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the re-gions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv)how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To per-form this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. Theresulting database, named‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 indi-vidual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluatedand transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insightsinto the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to sup-port the upcoming country-based United Nations global soil-erosion assessment in addition to helping to informsoil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is anopen-source database available to the entire user-community to develop research, rectify errors, andmakefutureexpansion
Soil erosion modelling: A bibliometric analysis
Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication\u27s CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper
Soil erosion modelling: A global review and statistical analysis
To gain a better understanding of the global application of soil erosion prediction models, we comprehensively
reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and
2017.We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions
within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv)
how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform
this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The
resulting database, named ‘Global Applications of Soil ErosionModelling Tracker (GASEMT)’, includes 3030 individual
modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471
articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated
and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights
into the state-of-the-art of soil- erosionmodels and model applicationsworldwide. This database intends to support
the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform
soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an
open-source database available to the entire user-community to develop research, rectify errors, andmake future
expansions
Soil erosion modelling: A bibliometric analysis
Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore,
soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion
hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics
that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed
bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted.
The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database
contains information about citation characteristics and publication type. Here, we investigated the impact of the
number of authors, the publication type and the selected journal on the number of citations. Generalized boosted
regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion
modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection
of the soil erosion model has the largest impact on the number of publication citations, followed by the
modelling scale and the publication’s CiteScore. Some of the other GASEMT database attributes such as model
calibration and validation have negligible influence on the number of citations according to the BRT model.
Although it is true that studies that conduct calibration, on average, received around 30% more citations, than
studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a
clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore,
soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the
research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an
additional focus should be given to field measurements, model calibration, performance assessment and uncertainty
of modelling results. The results of this study indicate that these GASEMT database attributes had
smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest
that these attributes should be given additional attention by the soil erosion modelling community. This study
provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate
the influence of their paper
Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression
In landslide susceptibility assessment, an important issue is the correct
identification of significant contributing factors, which leads to the
improvement of predictions regarding this type of geomorphologic processes.
In the scientific literature, different weightings are assigned to these
factors, but contain large variations. This study aims to identify the
spatial variability and range of variation for the coefficients of landslide
predictors in different geographical conditions. Four sectors of
15 km × 15 km (225 km<sup>2</sup>) were selected for analysis from
representative regions in Romania in terms of spatial extent of landslides,
situated both on the hilly areas (the Transylvanian Plateau and Moldavian
Plateau) and lower mountain region (Subcarpathians). The following factors
were taken into consideration: elevation, slope angle, slope height, terrain
curvature (mean, plan and profile), distance from drainage network, slope
aspect, land use, and lithology. For each sector, landslide inventory,
digital elevation model and thematic layers of the mentioned predictors were
achieved and integrated in a georeferenced environment. The logistic
regression was applied separately for the four study sectors as the statistical
method for assessing terrain landsliding susceptibility. Maps of landslide
susceptibility were produced, the values of which were classified by using
the natural breaks method (Jenks). The accuracy of the logistic regression
outcomes was evaluated using the ROC (receiver operating characteristic) curve and AUC (area under the curve) parameter, which show
values between 0.852 and 0.922 for training samples, and between 0.851 and
0.940 for validation samples. The values of coefficients are generally
confined within the limits specified by the scientific literature. In each
sector, landslide susceptibility is essentially related to some specific
predictors, such as the slope angle, land use, slope height, and lithology.
The study points out that the coefficients assigned to the landslide
predictors through logistic regression are capable to reveal some important
characteristics in landslide manifestation. The study also shows that the
logistic regression could be an alternative method to the current Romanian
methodology for landslide susceptibility and hazard mapping
Mapping viticultural potential in temperate climate areas. Case study: Bucium vineyard (Romania)
The paper presents the spatial
distribution of ecological suitability for
grape growing in Bucium vineyard
(Romania), viticultural area with ecological
characteristics representative for northern
vineyards. The study is based on a complex
methodology implying use of remote
sensing, Geographical Information
Systhems (GIS), climatic data, topographic
and pedologic maps. Research reveal the
low ecological potential of Bucium area,
specialized, traditionally, in white table
wines, sparkling wines and white quality
wines. Data analysis shows that 30% of
Bucium vineyard (281 ha of 928 ha) is
inappropriate, in terms of climatic
suitability, for vinifera varieties culture;
34% of the area (316 ha) has limited
ecological potential, enough to produce
white table wines and sparkling wines; 36%
of the area (331 ha) is suitable for quality
white wines. In the vineyard area was not
registered suitable conditions for quality red
wines production. Huglin’s heliothermal
index values shows that the vineyard has
climatic characteristics that allow culture of
wine varieties with early and medium
ripening. In terms of ecological suitability, it
appears that the most favorable conditions
offer Cetăţuia wine land, the eastern slope
of the Doi Peri hill, eastern slope of Vişani
hill, south-western slope of Bucium hill and
southern slope of Pietrăria wine land
Arable lands under the pressure of multiple land degradation processes. A global perspective
While agricultural systems are a major pillar in global food security, their productivity is currently threatened by many environmental issues triggered by anthropogenic climate change and human activities, such as land degradation. However, the planetary spatial footprint of land degradation processes on arable lands, which can be considered a major component of global agricultural systems, is still insufficiently well understood. This study analyzes the land degradation footprint on global arable lands, using complex geospatial data on certain major degradation processes, i.e. aridity, soil erosion, vegetation decline, soil salinization and soil organic carbon decline. By applying geostatistical techniques that are representative for identifying the incidence of the five land degradation processes in global arable lands, results showed that aridity is by far the largest singular pressure for these agricultural systems, affecting ~40% of the arable lands' area, which cover approximately 14 million km2 globally. It was found that soil erosion is another major degradation process, the unilateral impact of which affects ~20% of global arable systems. The results also showed that the two degradation processes simultaneously affect an additional ~7% of global arable lands, which makes this synergy the most common form of multiple pressure of land degradative conditions across the world's arable areas. The absolute statistical data showed that India, the United States, China, Brazil, Argentina, Russia and Australia are the most vulnerable countries in the world to the various pathways of arable land degradation. Also, in terms of percentages, statistical observations showed that African countries are the most heavily affected by arable system degradation. This study's findings can be useful for prioritizing agricultural management actions that can mitigate the negative effects of the two degradation processes or of others that currently affect many arable systems across the planet