A Review of Soil-Improving Cropping Systems for Soil Salinization

A major challenge of the Sustainable Development Goals linked to Agriculture, Food Security, and Nutrition, under the current global crop production paradigm, is that increasing crop yields often have negative environmental impacts. It is therefore urgent to develop and adopt optimal soil-improving cropping systems (SICS) that can allow us to decouple these system parameters. Soil salinization is a major environmental hazard that limits agricultural potential and is closely linked to agricultural mismanagement and water resources overexploitation, especially in arid climates. Here we review literature seeking to ameliorate the negative effect of soil salinization on crop productivity and conduct a global meta-analysis of 128 paired soil quality and yield observations from 30 studies. In this regard, we compared the effectivity of different SICS that aim to cope with soil salinization across 11 countries, in order to reveal those that are the most promising. The analysis shows that besides case-specific optimization of irrigation and drainage management, combinations of soil amendments, conditioners, and residue management can contribute to significant reductions of soil salinity while significantly increasing crop yields. These results highlight that conservation agriculture can also achieve the higher yields required for upscaling and sustaining crop production

Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion

© 2019 by the authors. Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements

Climate Change Impact on Photovoltaic Energy Output: The Case of Greece

Solar power is the third major renewable energy, constituting an increasingly important component of global future—low carbon—energy portfolio. Accurate climate information is essential for the conditions of solar energy production, maximization, and stable regulation and planning. Climate change impacts on energy output projections are thus of crucial importance. In this study the effect of projected changes in irradiance and temperature on the performance of photovoltaic systems in Greece is examined. Climate projections were obtained from 5 regional climate models (RCMs) under the A1B emissions scenario, for two future periods. The RCM data present systematic errors against observed values, resulting in the need of bias adjustment. The projected change in photovoltaic energy output was then estimated, considering changes in temperature and insolation. The spatiotemporal analysis indicates significant increase in mean annual temperature (up to 3.5°C) and mean total radiation (up to 5 W/m2) by 2100. The performance of photovoltaic systems exhibits a negative linear dependence on the projected temperature increase which is outweighed by the expected increase of total radiation resulting in an up to 4% increase in energy output