The main purpose of this study is to identify suitable potential areas for agricultural activities in the semi-arid terrestrial ecosystem in the Central Anatolia Region. MCDA was performed in fuzzy environment integrated with GIS techniques and different geostatistical interpolation models, which was chosen as the basis for the present study. A total of nine criteria were used, as four terrain properties and five soil features to identify potential sites suitable for agriculture lands in Central Anatolia which covers approximately 195,012.7 km. In order to assign weighting value for each criterion, FAHP approach was used to make sufficiently sensitive levels of importance of the criteria. DEM with 10 m pixel resolution used to determine the height and slope characteristics, digital geology and soil maps, CORINE land use/land cover, long-term meteorological data, and 4517 soil samples taken from the study area were used. It was identified that approximately 30.7% of the total area (59,921.8 ha) is very suitable and suitable for potential agriculture activities on S1 and S2 levels, 42.7% of the area is not suitable for agricultural uses, and only 27% of the area is marginally suitable for agricultural activities. Besides, it was identified that 34.8% of the area is slightly suitable.
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http://dx.doi.org/10.1038/s41598-020-79105-4 | DOI Listing |
Microorganisms
October 2024
Gansu Academy of Agri-Engineering Technology, Wuwei 733006, China.
As a vital component of the global carbon pool, soils in arid and semi-arid regions play a significant role in carbon sequestration. In the context of global warming, increasing temperatures and moisture levels promote the transformation of barren land into wetlands, enhancing carbon sinks. However, the overdevelopment of oases and excessive extraction of groundwater lead to the opposite effect, reducing carbon sequestration.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
November 2024
School of Life Sciences/Hebei Basic Science Center for Biotic Interaction, Institute of Life Science and Green Development, Hebei University, Baoding, Hebei 071002, China.
Enhancing terrestrial carbon (C) stock through ecological restoration, one of the prominent approaches for natural climate solutions, is conventionally considered to be achieved through an ecological pathway, i.e., increased plant C uptake.
View Article and Find Full Text PDFEnviron Monit Assess
November 2024
Department of Geography, School of Arts, The University of Jordan, Amman, Jordan.
J Environ Manage
December 2024
School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Center of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China. Electronic address:
Climate change has triggered more frequent drought occurrence, which can have devastating impacts on the ecosystem functions. Studies on vegetation behavior during droughts have mainly focused on arid/semi-arid regions, yet the ecological and vegetation responses during drought in humid regions remain unclear. Here we systematically evaluated the evolution of the historic drought occurred in the humid Pearl River Basin in 2021 and quantified the vegetation responses using a multitude of vegetation indicators.
View Article and Find Full Text PDFJ Environ Manage
November 2024
Key Laboratory of Earth System Numerical Modeling and Application/College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF).
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