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18 results on '"Samani, A. N."'

3. Evaluation of Homocysteine Levels in Patients with Polycystic Ovarian Syndrome

Author

Salehpour, S., Manzor-Al-Ajdad, O., Samani, E. N., and Ashraf Abadi

Subjects
lcsh:R5-920, Gynecology and Female Infertility, endocrine system diseases, polycystic ovary syndrome, homocysteine blood, cardiovascular disease, nutritional and metabolic diseases, Original Article, lcsh:Medicine (General), female genital diseases and pregnancy complications, Endocrinology and Metabolism
Abstract

Background To determine the level of plasma homocysteine in patients with polycystic ovary syndrome (PCOS) compared with healthy controls. Materials and Methods In this prospective case-control study on 85 PCOS women and 83 controls matched by body mass index (BMI), homocysteine levels were assessed. Results The mean level of homocysteine was 16.25 ± 11.94 μmol/L in patients with PCOS and 11.58 ± 3.82 μmol/L in controls (p=0.002). Patients with PCOS had a significantly higher risk for hyperhomocysteinemia compared with BMI-matched control women. Conclusion These data suggest that homocysteine levels are elevated in the PCOS population. Further studies are needed to characterize this relationship.

Published
2011

7. Retinal Layer Abnormalities as Biomarkers of Schizophrenia.

Author

Samani, Niraj N., Proudlock, Frank A., Siram, Vasantha, Suraweera, Chathurie, Hutchinson, Claire, Nelson, Christopher P., Al-Uzri, Mohammed, and Gottlob, Irene

Subjects
DIAGNOSIS of schizophrenia, RETINAL ganglion cells, ANTIPSYCHOTIC agents, BIOMARKERS, EYE examination, PHOTORECEPTORS, REGRESSION analysis, RETINAL diseases, STATISTICS, DATA analysis, OPTICAL coherence tomography, SEVERITY of illness index, DISEASE duration, ANATOMY
Abstract

Objective: Schizophrenia is associated with several brain deficits, as well as visual processing deficits, but clinically useful biomarkers are elusive. We hypothesized that retinal layer changes, noninvasively visualized using spectraldomain optical coherence tomography (SD-OCT), may represent a possible "window" to these abnormalities. Methods: A Leica EnvisuTM SD-OCT device was used to obtain high-resolution central foveal B-scans in both eyes of 35 patients with schizophrenia and 50 demographically matched controls. Manual retinal layer segmentation was performed to acquire individual and combined layer thickness measurements in 3 macular regions. Contrast sensitivity was measured at 3 spatial frequencies in a subgroup of each cohort. Differences were compared using adjusted linear models and significantly different layer measures in patients underwent Spearman Rank correlations with contrast sensitivity, quantified symptoms severity, disease duration, and antipsychotic medication dose. Results: Total retinal and photoreceptor complex thickness was reduced in all regions in patients (P < .0001). Segmentation revealed consistent thinning of the outer nuclear layer (P < .001) and inner segment layer (P < .05), as well as a pattern of parafoveal ganglion cell changes. Low spatial frequency contrast sensitivity was reduced in patients (P = .002) and correlated with temporal parafoveal ganglion cell complex thinning (R = .48, P = .01). Negative symptom severity was inversely correlated with foveal photoreceptor complex thickness (R = -.54, P = .001) and outer nuclear layer thickness (R = -.47, P = .005). Conclusions: Our novel findings demonstrate considerable retinal layer abnormalities in schizophrenia that are related to clinical features and visual function. With time, SD-OCT could provide easily-measurable biomarkers to facilitate clinical assessment and further our understanding of the disease. [ABSTRACT FROM AUTHOR]

Published
2018
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8. Application of multiple sediment fingerprinting techniques to determine the sediment source contribution of gully erosion: Review and case study from Boushehr province, southwestern Iran

Author

Samani, Aliakbar N., Wasson, Robert J., Malekian, Arash, Samani, Aliakbar N., Wasson, Robert J., and Malekian, Arash

Abstract

Soil erosion by water is one of the most important causes of land degradation in arid and semi-arid regions. Quantitative determination of the relative contributions of sediment sources within catchments is an essential task before developing any appropriate management strategy in order to control soil erosion and sediment transport. In this paper a multi-parameter fingerprinting method is used to determine the sediment contribution of gully erosion (Scg) in three catchments in Iran, with different areas and drainage densities. Tracers including carbon, nitrogen, C/N, phosphorus and 137Cs which provide a clear distinction between subsoil and topsoil were used. The mean value of Scg, the efficiency of the composite tracer, the variation of Scg, and finally the accuracy of model calculations for each tracer are discussed. Although all tracers showed a considerable Scg (between 84 and 99%), the variation and accuracy of mixed models varied noticeably. Furthermore, the residual root mean square error (RRMSE) showed that 137Cs is the most valuable informative tracer while the composite tracers combining organic tracers and 137Cs presented the lowest accuracy. The results also demonstrate that the optimum combination of tracers in each region might be different. Combining the value of Scg with catchment area and geomorphic parameters showed that drainage density and catchment area are the most important factors on Scg variation. However, other physical factors such as gully density and hillslope length should also be taken into account to determine where gully erosion becomes a dominant soil erosion process. Land managers, who rely on results of erosion models which mainly focus on rill and sheet erosion, should consider the results, which demonstrate the importance of gullies as a major sediment source.

Published
2011

11. Prediction of physical properties of Al2TiO5based ceramics containing micro and nano size oxide additives by using artificial neural network

Author

Shokuhfar, A., Samani, M. N., Naserifar, N., Heidary, P., and Naderi, G.

Abstract

Recently, artificial neural networks ANN are being used as an interdisciplinary tool in many applications. There are various training algorithms used in neural network applications. The aim of this study is to investigate the effect of various training algorithms on the learning performance of the neural networks in the prediction and modeling of the effect of micron and nano size oxide material additives on reaction sintering and physical properties of Al2TiO5based ceramics. Aluminumtitanate tialite based ceramics have found widespread applications due to their good thermal shock resistance and low thermal expansion. Eutectoid decomposition in the initial oxides and low mechanical strength limit the wellknown properties of aluminumtitanate. In the present work, first, good stabilizing behavior was achieved by addition of micron size talc and appropriate properties were obtained by adding nano boehmite and colloidal silica that result in mullite phase formation. Then, the effect of the weight percentages of these different additives and also the temperature on the bulk density was predicted with four different training algorithms using a backpropagation neural network. The training sets for the neural network were selected from experimental results. After training ANN, a regression analysis was used to check the system accuracy for each training algorithm. In conclusion, Levenberg–Marquardt LM learning algorithm gave the best prediction for the bulk density behavior of Al2TiO5based ceramics. The response surfaces between the response variables, i.e. weight percent of additives and temperature of the tialite and the processing parameter are presented. The trained artificial neural network can be used for optimizing the sintering process of Al2TiO5based ceramics.

Published
2009
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12. Soil erosion modelling: A global review and statistical analysis

Author

Borrelli, P., Alewell, C., Alvarez, Pablo, Anache, J. A. A., Baartman, J., Ballabio, C., Bezak, N., Biddoccu, M., Cerdà, A., Chalise, D., Chen, S., Chen, W., De Girolamo, A. M., Gessesse, G. D., Deumlich, D., Diodato, N., Efthimiou, N., Erpul, G., Fiener, P., Freppaz, M., Gentile, F., Gericke, A., Haregeweyn, N., Hu, B., Jeanneau, A., Kaffas, K., Kiani-Harchegani, M., Villuendas, I. L., Li, C., Lombardo, L., López-Vicente, M., Lucas-Borja, M. E., Märker, M., Matthews, F., Miao, C., Mikoš, M., Modugno, S., Möller, M., Naipal, V., Nearing, M., Owusu, S., Panday, D., Patault, E., Patriche, C. V., Poggio, L., Portes, R., Quijano, L., Rahdari, M. R., Renima, M., Ricci, G. F., Rodrigo-Comino, J., Saia, S., Samani, A. N., Schillaci, C., Syrris, V., Kim, H. S., Spinola, D. N., Oliveira, P. T., Teng, H., Thapa, R., Vantas, K., Vieira, D., Yang, J. E., Yin, S., Zema, D. A., Zhao, G., and Panagos, P.

Subjects
Erosion rates, Land sustainability, Land degradation, 15. Life on land, GIS, Policy support, Modelling
Abstract

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, andmakefutureexpansions

14. Soil erosion modelling: A bibliometric analysis

Author

Bezak, N., Mikoš, M., Borrelli, P., Alewell, C., Alvarez, P., Anache, J. A. A., Baartman, J., Ballabio, C., Biddoccu, M., Cerdà, A., Chalise, D., Chen, S., Chen, W., De Girolamo, A. M., Gessesse, G. D., Deumlich, D., Diodato, N., Efthimiou, N., Erpul, G., Fiener, P., Freppaz, M., Gentile, F., Gericke, A., Haregeweyn, N., Hu, B., Jeanneau, A., Kaffas, K., Kiani-Harchegani, M., Villuendas, I. L., Li, C., Lombardo, L., López-Vicente, M., Lucas-Borja, M. E., Maerker, M., Miao, C., Modugno, S., Möller, M., Naipal, V., Nearing, M., Owusu, S., Panday, D., Patault, E., Patriche, C. V., Poggio, L., Portes, R., Quijano, L., Rahdari, M. R., Renima, M., Ricci, G. F., Rodrigo-Comino, J., Saia, S., Samani, A. N., Schillaci, C., Syrris, V., Kim, H. S., Spinola, D. N., Oliveira, P. T., Teng, H., Thapa, R., Vantas, K., Vieira, D., Yang, J. E., Yin, S., Zema, D. A., Zhao, G., and Panagos, P.

Subjects
Research impact, Citation analysis, 13. Climate action, Soil erosion modelling, Systematic literature review, Participatory network, 15. Life on land
Abstract

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.

17. Soil erosion modelling: A bibliometric analysis

Author

Chiyuan Miao, Markus Möller, Cristiano Ballabio, Peter Fiener, Ivan Lizaga Villuendas, Mark A. Nearing, Nikolaos Efthimiou, Jae E. Yang, Christine Alewell, Francesco Gentile, Anna Maria De Girolamo, Aliakbar Nazari Samani, Andreas Gericke, Paulo Tarso Sanches de Oliveira, Amelie Jeanneau, Pablo Alvarez, Konstantinos Kaffas, Diogo Noses Spinola, Marcella Biddoccu, Nejc Bezak, Pasquale Borrelli, Guangju Zhao, Michele Freppaz, Gizaw Desta Gessesse, Jesús Rodrigo-Comino, Sergio Saia, Luigi Lombardo, Diana Vieira, Hongfen Teng, Mahboobeh Kiani-Harchegani, Walter W. Chen, Nazzareno Diodato, Changjia Li, Calogero Schillaci, Detlef Deumlich, Shuiqing Yin, Raquel de Castro Portes, Gunay Erpul, Jamil Alexandre Ayach Anache, Laura Quijano, Konstantinos Vantas, Nigussie Haregeweyn, Artemi Cerdà, Mohammed Renima, Sirio Modugno, Laura Poggio, Cristian Valeriu Patriche, Edouard Patault, Manuel Esteban Lucas-Borja, Vasileios Syrris, Demetrio Antonio Zema, Jantiene Baartman, Mohammad Reza Rahdari, Michael Maerker, Devraj Chalise, Bifeng Hu, Hyuck Soo Kim, Giovanni Francesco Ricci, Dinesh Panday, Matjaž Mikoš, Stephen Owusu, Panos Panagos, Songchao Chen, Victoria Naipal, Manuel López-Vicente, Resham Thapa, Department of Earth Systems Analysis, UT-I-ITC-4DEarth, Faculty of Geo-Information Science and Earth Observation, Bezak, N., Mikos, M., Borrelli, P., Alewell, C., Alvarez, P., Anache, J. A. A., Baartman, J., Ballabio, C., Biddoccu, M., Cerda, A., Chalise, D., Chen, S., Chen, W., De Girolamo, A. M., Gessesse, G. D., Deumlich, D., Diodato, N., Efthimiou, N., Erpul, G., Fiener, P., Freppaz, M., Gentile, F., Gericke, A., Haregeweyn, N., Hu, B., Jeanneau, A., Kaffas, K., Kiani-Harchegani, M., Villuendas, I. L., Li, C., Lombardo, L., Lopez-Vicente, M., Lucas-Borja, M. E., Maerker, M., Miao, C., Modugno, S., Moller, M., Naipal, V., Nearing, M., Owusu, S., Panday, D., Patault, E., Patriche, C. V., Poggio, L., Portes, R., Quijano, L., Rahdari, M. R., Renima, M., Ricci, G. F., Rodrigo-Comino, J., Saia, S., Samani, A. N., Schillaci, C., Syrris, V., Kim, H. S., Spinola, D. N., Oliveira, P. T., Teng, H., Thapa, R., Vantas, K., Vieira, D., Yang, J. E., Yin, S., Zema, D. A., Zhao, G., Panagos, P., Slovenian Research Agency, Fundação para a Ciência e a Tecnologia (Portugal), Korea Environmental Industry & Technology Institute, Ministry of Science and Technology (Taiwan), Lizaga Villuendas, Iván [0000-0003-4372-5901], Quijano Gaudes, Laura [0000-0002-2334-2818], Lizaga Villuendas, Iván, Quijano Gaudes, Laura, University of Ljubljana, University of Pavia, Kangwon National University, University of Basel (Unibas), Karlsruhe Institute of Technology (KIT), National University of Loja, University of São Paulo (USP), FEDERAL UNIVERSITY OF MATO GROSSO DO SUL CAMPO GRANDE BRA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Wageningen University and Research [Wageningen] (WUR), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Sciences and Technologies for Sustainable Energy and Mobility ( (STEMS)), National Research Council of Italy, University of Valencia,Valencia, SCHOOL OF ENVIRONMENTAL AND RURAL SCIENCE UNIVERSITY OF NEW ENGLAND ARMIDALE AUS, InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), National Taipei University of technology [Taipei] (TAIPEI TECH), National Taipei University of Technology, WATER RESEARCH INSTITUTE NATIONAL RESEARCH COUNCIL ROME, ITA, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Leibniz-Center for Agricultural Landscape Research Muencheberg (ZALF), Met European Research Observatory (MetEROBS), Czech University of Life Sciences Prague (CZU), University of Ankara, Universität Augsburg [Augsburg], University of Turin, University of Bari Aldo Moro (UNIBA), Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Tottori University, Jiangxi University of Finance and Economics (JUFE), University of Adelaide, Free University of Bozen-Bolzano, Yazd University, Spanish National Research Council (CSIC), Beijing Normal University (BNU), University of Twente [Netherlands], Wageningen Environmental Research (Alterra), University of Castilla-La Mancha (UCLM), World Food Programme (WFP), United Nations, University of Leicester, Julius Kühn Institute (JKI), Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Southwest Watershed Research Center, USDA-ARS : Agricultural Research Service, Soil Research Institute, University of Nebraska [Lincoln], University of Nebraska System, Normandie Université (NU), Romanian Academy, World Soil Information (ISRIC), Minas Gerais State University, Université Catholique de Louvain = Catholic University of Louvain (UCL), University of Torbat Heydarieh, University Hassiba Benbouali of Chlef, Trier University of Applied Sciences, University of Pisa - Università di Pisa, University of Tehran, University of Milan, University of Alaska [Fairbanks] (UAF), Wuhan Institute of Technology, Wuhan University [China], University of Maryland [Baltimore], Aristotle University of Thessaloniki, Department of Environment and Planning (DAO), Centre for Environmental and Marine Studies (CESAM), University of Aveiro, Aveiro, Portugal, Mediterranean University of Reggio Calabria, and Northwest A and F University

Subjects
Research impact, Calibration (statistics), Geography & travel, Decision tree, Participatory network, Agricultural engineering, [SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study, 010501 environmental sciences, Participatory modeling, 01 natural sciences, Biochemistry, Bibliometric, ITC-HYBRID, 03 medical and health sciences, Soil, 0302 clinical medicine, Citation analysis, Benchmark (surveying), Soil erosion modelling, Systematic literature review, Agriculture, Publications, Bibliometrics, Soil Erosion, ddc:550, 030212 general & internal medicine, 0105 earth and related environmental sciences, General Environmental Science, ddc:910, WIMEK, Bodemfysica en Landbeheer, 15. Life on land, PE&RC, Bibliographic coupling, Soil Physics and Land Management, 13. Climate action, Citation analysi, ITC-ISI-JOURNAL-ARTICLE, Erosion, Environmental science, Publication, Scale (map), ISRIC - World Soil Information
Abstract

16 Pags.- 12 Figs.- 8 Tabls., 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., Nejc Bezak and Matjaž Mikoš would like to acknowledge the support of the Slovenian Research Agency through grant P2-0180. Diana Vieira is funded by national funds (OE), through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen - DL57/2016 (CDL-CTTRI-97-ARH/2018 - REF.191-97-ARH/2018), and acknowledges CESAM financial support of through (UIDP/50017/2020+UIDB/50017/2020). Jae E. Yang and Pasquale Borrelli are funded by the EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea (Grant No. 2019002820004). Walter Chen is funded by the Ministry of Science and Technology (Taiwan) Research Project (Grant Number MOST 109-2121-M-027-001).

Published
2021
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18. Soil erosion modelling: a global review and statistical analysis

Author

Marcella Biddoccu, Matjaž Mikoš, Stephen Owusu, Panos Panagos, Songchao Chen, Cristian Valeriu Patriche, Amelie Jeanneau, Aliakbar Nazari Samani, Manuel Esteban Lucas-Borja, Shuiqing Yin, Raquel de Castro Portes, Mahboobeh Kiani-Harchegani, Artemi Cerdà, Laura Poggio, Bifeng Hu, Peter Fiener, Mark A. Nearing, Diogo Noses Spinola, Michele Freppaz, Francis Matthews, Jantiene Baartman, Walter W. Chen, Pablo Alvarez, Konstantinos Kaffas, Nejc Bezak, Pasquale Borrelli, Anna Maria De Girolamo, Guangju Zhao, Andreas Gericke, Nikolaos Efthimiou, Changjia Li, Hyuck Soo Kim, Konstantinos Vantas, Paulo Tarso Sanches de Oliveira, Sergio Saia, Luigi Lombardo, Nazzareno Diodato, Nigussie Haregeweyn, Michael Märker, Gizaw Desta Gessesse, Jesús Rodrigo-Comino, Jae E. Yang, Victoria Naipal, Markus Möller, Cristiano Ballabio, Christine Alewell, Detlef Deumlich, Resham Thapa, Devraj Chalise, Vasileios Syrris, Chiyuan Miao, Manuel López-Vicente, Francesco Gentile, Laura Quijano, Diana Vieira, Sirio Modugno, Gunay Erpul, Calogero Schillaci, Mohammed Renima, Edouard Patault, Giovanni Francesco Ricci, Jamil Alexandre Ayach Anache, Demetrio Antonio Zema, Mohammad Reza Rahdari, Dinesh Panday, Hongfen Teng, Ivan Lizaga Villuendas, Borrelli, P., Alewell, C., Alvarez, P., Anache, J. A. A., Baartman, J., Ballabio, C., Bezak, N., Biddoccu, M., Cerda, A., Chalise, D., Chen, S., Chen, W., De Girolamo, A. M., Gessesse, G. D., Deumlich, D., Diodato, N., Efthimiou, N., Erpul, G., Fiener, P., Freppaz, M., Gentile, F., Gericke, A., Haregeweyn, N., Hu, B., Jeanneau, A., Kaffas, K., Kiani-Harchegani, M., Villuendas, I. L., Li, C., Lombardo, L., Lopez-Vicente, M., Lucas-Borja, M. E., Marker, M., Matthews, F., Miao, C., Mikos, M., Modugno, S., Moller, M., Naipal, V., Nearing, M., Owusu, S., Panday, D., Patault, E., Patriche, C. V., Poggio, L., Portes, R., Quijano, L., Rahdari, M. R., Renima, M., Ricci, G. F., Rodrigo-Comino, J., Saia, S., Samani, A. N., Schillaci, C., Syrris, V., Kim, H. S., Spinola, D. N., Oliveira, P. T., Teng, H., Thapa, R., Vantas, K., Vieira, D., Yang, J. E., Yin, S., Zema, D. A., Zhao, G., Panagos, P., InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Korea Environmental Industry & Technology Institute, Fundação para a Ciência e a Tecnologia (Portugal), Ministry of Science and Technology (Taiwan), Slovenian Research Agency, Lizaga Villuendas, Iván, Quijano Gaudes, Laura, López-Vicente, Manuel, Lizaga Villuendas, Iván [0000-0003-4372-5901], Quijano Gaudes, Laura [0000-0002-2334-2818], and López-Vicente, Manuel [0000-0002-6379-8844]

Subjects
Research literature, Environmental Engineering, Erosion rates, 010504 meteorology & atmospheric sciences, Computer science, Geography & travel, Review, [SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study, 010501 environmental sciences, Erosion rate, 01 natural sciences, Policy support, Modelling, ITC-HYBRID, GIS, Land degradation, Land sustainability, ddc:550, Environmental Chemistry, Statistical analysis, Waste Management and Disposal, 0105 earth and related environmental sciences, ddc:910, WIMEK, business.industry, Environmental resource management, Collective intelligence, Bodemfysica en Landbeheer, 15. Life on land, PE&RC, Pollution, Soil Physics and Land Management, ITC-ISI-JOURNAL-ARTICLE, Sustainability, Erosion, business, ISRIC - World Soil Information, Predictive modelling
Abstract

40 Pags.- 10 Figs.- 2 Tabls.- Suppl. Informat. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697, 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 Erosion Modelling 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- erosion models and model applications worldwide. 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, and make future expansions., Jae E. Yang and Pasquale Borrelli are funded by the EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea (Grant No. 2019002820004). Diana Vieira is funded by national funds (OE), through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen - DL57/2016 (CDL-CTTRI-97-ARH/2018 - REF.191-97-ARH/2018), and acknowledges CESAM financial support of through (UIDP/50017/2020+UIDB/50017/2020). Walter Chen is funded by the Ministry of Science and Technology (Taiwan) Research Project (Grant Number MOST 109-2121-M-027-001). Nejc Bezak and Matjaž Mikoš would like to acknowledge the support of the Slovenian Research Agency through grant P2-0180.

Published
2021
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